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\n  \n 2024\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Are large language models superhuman chemists?.\n \n \n \n \n\n\n \n Mirza, A.; Alampara, N.; Kunchapu, S.; Emoekabu, B.; Krishnan, A.; Wilhelmi, M.; Okereke, M.; Eberhardt, J.; Elahi, A. M.; Greiner, M.; Holick, C. T.; Gupta, T.; Asgari, M.; Glaubitz, C.; Klepsch, L. C.; Köster, Y.; Meyer, J.; Miret, S.; Hoffmann, T.; Kreth, F. A.; Ringleb, M.; Roesner, N.; Schubert, U. S.; Stafast, L. M.; Wonanke, D.; Pieler, M.; Schwaller, P.; and Jablonka, K. M.\n\n\n \n\n\n\n April 2024.\n arXiv:2404.01475 [cond-mat, physics:physics]\n\n\n\n
\n\n\n\n \n \n \"ArePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{mirza_are_2024,\n\ttitle = {Are large language models superhuman chemists?},\n\turl = {http://arxiv.org/abs/2404.01475},\n\tdoi = {10.48550/arXiv.2404.01475},\n\tabstract = {Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. This is relevant for the chemical sciences, which face the problem of small and diverse datasets that are frequently in the form of text. LLMs have shown promise in addressing these issues and are increasingly being harnessed to predict chemical properties, optimize reactions, and even design and conduct experiments autonomously. However, we still have only a very limited systematic understanding of the chemical reasoning capabilities of LLMs, which would be required to improve models and mitigate potential harms. Here, we introduce "ChemBench," an automated framework designed to rigorously evaluate the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of human chemists. We curated more than 7,000 question-answer pairs for a wide array of subfields of the chemical sciences, evaluated leading open and closed-source LLMs, and found that the best models outperformed the best human chemists in our study on average. The models, however, struggle with some chemical reasoning tasks that are easy for human experts and provide overconfident, misleading predictions, such as about chemicals' safety profiles. These findings underscore the dual reality that, although LLMs demonstrate remarkable proficiency in chemical tasks, further research is critical to enhancing their safety and utility in chemical sciences. Our findings also indicate a need for adaptations to chemistry curricula and highlight the importance of continuing to develop evaluation frameworks to improve safe and useful LLMs.},\n\turldate = {2024-04-13},\n\tpublisher = {arXiv},\n\tauthor = {Mirza, Adrian and Alampara, Nawaf and Kunchapu, Sreekanth and Emoekabu, Benedict and Krishnan, Aswanth and Wilhelmi, Mara and Okereke, Macjonathan and Eberhardt, Juliane and Elahi, Amir Mohammad and Greiner, Maximilian and Holick, Caroline T. and Gupta, Tanya and Asgari, Mehrdad and Glaubitz, Christina and Klepsch, Lea C. and Köster, Yannik and Meyer, Jakob and Miret, Santiago and Hoffmann, Tim and Kreth, Fabian Alexander and Ringleb, Michael and Roesner, Nicole and Schubert, Ulrich S. and Stafast, Leanne M. and Wonanke, Dinga and Pieler, Michael and Schwaller, Philippe and Jablonka, Kevin Maik},\n\tmonth = apr,\n\tyear = {2024},\n\tnote = {arXiv:2404.01475 [cond-mat, physics:physics]},\n\tkeywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Condensed Matter - Materials Science, Physics - Chemical Physics},\n}\n\n
\n
\n\n\n
\n Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. This is relevant for the chemical sciences, which face the problem of small and diverse datasets that are frequently in the form of text. LLMs have shown promise in addressing these issues and are increasingly being harnessed to predict chemical properties, optimize reactions, and even design and conduct experiments autonomously. However, we still have only a very limited systematic understanding of the chemical reasoning capabilities of LLMs, which would be required to improve models and mitigate potential harms. Here, we introduce \"ChemBench,\" an automated framework designed to rigorously evaluate the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of human chemists. We curated more than 7,000 question-answer pairs for a wide array of subfields of the chemical sciences, evaluated leading open and closed-source LLMs, and found that the best models outperformed the best human chemists in our study on average. The models, however, struggle with some chemical reasoning tasks that are easy for human experts and provide overconfident, misleading predictions, such as about chemicals' safety profiles. These findings underscore the dual reality that, although LLMs demonstrate remarkable proficiency in chemical tasks, further research is critical to enhancing their safety and utility in chemical sciences. Our findings also indicate a need for adaptations to chemistry curricula and highlight the importance of continuing to develop evaluation frameworks to improve safe and useful LLMs.\n
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\n \n\n \n \n \n \n \n LLMs can Design Sustainable Concrete -a Systematic Benchmark (re-submitted version).\n \n \n \n\n\n \n Völker, C.; Rug, T.; Jablonka, K.; and Kruschwitz, S.\n\n\n \n\n\n\n January 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@book{volker_llms_2024,\n\ttitle = {{LLMs} can {Design} {Sustainable} {Concrete} -a {Systematic} {Benchmark} (re-submitted version)},\n\tabstract = {In the context of a circular building material economy, the complexity of resource flows and the variability of material composition pose significant challenges. This study demonstrates how Large Language Models (LLMs) can advance material design by adopting a Knowledge-Driven Design (KDD) approach that outperforms traditional Data-Driven Design (DDD) methods. Our focus is on designing alkali-activated concrete (AAC) mix designs, an environmentally friendly alternative to conventional Portland cement-based concrete. GPT-3.5 Turbo and GPT-4 Turbo enable using fuzzy design knowledge as previously untapped input data modality. A key aspect of our research is to improve the performance of the LLMs in post-training. We use strategies such as refining prompt context, extending test time, and including a verifier.The study's systematic benchmarks are based on 240 AAC formulations extracted from the literature. The target was on achieving maximum compressive strength through an adaptive design approach over multiple development cycles. We compare these results to the traditional DDD baseline methods. KDD outperforms conventional methods by providing robust initial predictions and demonstrating effective adaptability informed by laboratory validation data, culminating in the development of high-quality AAC formulations. These results provide valuable insight into the capabilities of chat-based LLMs in managing complex material formulations, which are particularly beneficial in situations where traditional DDD is impractical due to data collection issues. With natural language as the basis the KDD is intuitively accessible to domain experts. The methodology and results of this study have significant implications for the field of materials science, particularly in the context of a circular economy, and pave the way for innovative applications of LLMs in various scientific fields.},\n\tauthor = {Völker, Christoph and Rug, Tehseen and Jablonka, Kevin and Kruschwitz, Sabine},\n\tmonth = jan,\n\tyear = {2024},\n\tdoi = {10.13140/RG.2.2.33795.27686},\n}\n\n
\n
\n\n\n
\n In the context of a circular building material economy, the complexity of resource flows and the variability of material composition pose significant challenges. This study demonstrates how Large Language Models (LLMs) can advance material design by adopting a Knowledge-Driven Design (KDD) approach that outperforms traditional Data-Driven Design (DDD) methods. Our focus is on designing alkali-activated concrete (AAC) mix designs, an environmentally friendly alternative to conventional Portland cement-based concrete. GPT-3.5 Turbo and GPT-4 Turbo enable using fuzzy design knowledge as previously untapped input data modality. A key aspect of our research is to improve the performance of the LLMs in post-training. We use strategies such as refining prompt context, extending test time, and including a verifier.The study's systematic benchmarks are based on 240 AAC formulations extracted from the literature. The target was on achieving maximum compressive strength through an adaptive design approach over multiple development cycles. We compare these results to the traditional DDD baseline methods. KDD outperforms conventional methods by providing robust initial predictions and demonstrating effective adaptability informed by laboratory validation data, culminating in the development of high-quality AAC formulations. These results provide valuable insight into the capabilities of chat-based LLMs in managing complex material formulations, which are particularly beneficial in situations where traditional DDD is impractical due to data collection issues. With natural language as the basis the KDD is intuitively accessible to domain experts. The methodology and results of this study have significant implications for the field of materials science, particularly in the context of a circular economy, and pave the way for innovative applications of LLMs in various scientific fields.\n
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\n \n\n \n \n \n \n \n \n High throughput screening.\n \n \n \n \n\n\n \n Yoo, B.; and Jablonka, K.\n\n\n \n\n\n\n January 2024.\n \n\n\n\n
\n\n\n\n \n \n \"HighPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@patent{yoo_high_2024,\n\ttitle = {High throughput screening},\n\turl = {https://patents.google.com/patent/US20240019845A1/en},\n\tnationality = {US},\n\tassignee = {BASF SE, BASF Corp},\n\tnumber = {US20240019845A1},\n\turldate = {2024-02-15},\n\tauthor = {Yoo, Brian and Jablonka, Kevin},\n\tmonth = jan,\n\tyear = {2024},\n\tkeywords = {characteristic, materials, pareto, properties, provisional},\n}\n\n
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\n \n\n \n \n \n \n \n \n Leveraging large language models for predictive chemistry.\n \n \n \n \n\n\n \n Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; and Smit, B.\n\n\n \n\n\n\n Nature Machine Intelligence,1–9. February 2024.\n Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n \n \"LeveragingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{jablonka_leveraging_2024,\n\ttitle = {Leveraging large language models for predictive chemistry},\n\tcopyright = {2024 The Author(s)},\n\tissn = {2522-5839},\n\turl = {https://www.nature.com/articles/s42256-023-00788-1},\n\tdoi = {10.1038/s42256-023-00788-1},\n\tabstract = {Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop. Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning it to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine learning models for many applications spanning the properties of molecules and materials to the yield of chemical reactions. Surprisingly, our fine-tuned version of GPT-3 can perform comparably to or even outperform conventional machine learning techniques, in particular in the low-data limit. In addition, we can perform inverse design by simply inverting the questions. The ease of use and high performance, especially for small datasets, can impact the fundamental approach to using machine learning in the chemical and material sciences. In addition to a literature search, querying a pre-trained large language model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundation models, or to provide a baseline for predictive tasks.},\n\tlanguage = {en},\n\turldate = {2024-02-15},\n\tjournal = {Nature Machine Intelligence},\n\tauthor = {Jablonka, Kevin Maik and Schwaller, Philippe and Ortega-Guerrero, Andres and Smit, Berend},\n\tmonth = feb,\n\tyear = {2024},\n\tnote = {Publisher: Nature Publishing Group},\n\tkeywords = {Chemistry, Computational science},\n\tpages = {1--9},\n}\n\n
\n
\n\n\n
\n Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop. Here we show that GPT-3, a large language model trained on vast amounts of text extracted from the Internet, can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning it to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine learning models for many applications spanning the properties of molecules and materials to the yield of chemical reactions. Surprisingly, our fine-tuned version of GPT-3 can perform comparably to or even outperform conventional machine learning techniques, in particular in the low-data limit. In addition, we can perform inverse design by simply inverting the questions. The ease of use and high performance, especially for small datasets, can impact the fundamental approach to using machine learning in the chemical and material sciences. In addition to a literature search, querying a pre-trained large language model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundation models, or to provide a baseline for predictive tasks.\n
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\n \n\n \n \n \n \n \n \n Prioritizing Mentorship as Scientific Leaders.\n \n \n \n \n\n\n \n Deng, J. M.; Ahmed, S. E.; Awoonor-Williams, E.; Banerjee, P.; Barecka, M. H.; Bickerton, L. E.; Di Pietro, S. A.; Dorn, S. K.; Jablonka, K. M.; Laudadio, G.; Kreidt, E.; Mannochio-Russo, H.; Terra, J.; Wilkins, O. H.; Yerneni, S. S.; and Yusuf, M.\n\n\n \n\n\n\n ACS Central Science. January 2024.\n Publisher: American Chemical Society\n\n\n\n
\n\n\n\n \n \n \"PrioritizingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{deng_prioritizing_2024,\n\ttitle = {Prioritizing {Mentorship} as {Scientific} {Leaders}},\n\tissn = {2374-7943},\n\turl = {https://doi.org/10.1021/acscentsci.3c00500},\n\tdoi = {10.1021/acscentsci.3c00500},\n\turldate = {2024-01-20},\n\tjournal = {ACS Central Science},\n\tauthor = {Deng, Jacky M. and Ahmed, Salma Elgaili and Awoonor-Williams, Ernest and Banerjee, Progna and Barecka, Magda H. and Bickerton, Laura E. and Di Pietro, Silvina A. and Dorn, Stanna K. and Jablonka, Kevin Maik and Laudadio, Gabriele and Kreidt, Elisabeth and Mannochio-Russo, Helena and Terra, Júlio and Wilkins, Olivia Harper and Yerneni, Saigopalakrishna S. and Yusuf, Maha},\n\tmonth = jan,\n\tyear = {2024},\n\tnote = {Publisher: American Chemical Society},\n}\n\n
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\n \n\n \n \n \n \n \n \n Using imine chemistry for post-synthetical covalent chiralization of MOF surfaces.\n \n \n \n \n\n\n \n Novotny, B. Á.; Majumdar, S.; Ortega-Guerrero, A.; Jablonka, K. M.; Moubarak, E.; Gasilova, N.; and Smit, B.\n\n\n \n\n\n\n Technical Report Chemistry, January 2024.\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@techreport{novotny_using_2024,\n\ttype = {preprint},\n\ttitle = {Using imine chemistry for post-synthetical covalent chiralization of {MOF} surfaces},\n\turl = {https://chemrxiv.org/engage/chemrxiv/article-details/65a13e0466c13817294a4a0e},\n\tabstract = {Homochiral metal-organic frameworks (MOFs) are exceptional media for heterogenous enantiodifferentation processes. Their rational design, however, is challenging. An available solution for this is moving from constitutive, structure-bearing, homochiral ligands to separating chiral selector and structural roles. This introduces combinatorial modulability, which is the independent change of parts. Such modulability circumvents limitations of designability. Further considerations narrow emphasis to covalent post-synthetic methods. Covalent post-synthetic methods to introduce chirality are reported broadly, yet the diversity of pertaining linkage chemistry is modest. The present work explored the adaptation of imine chemistry for post-synthetic chiralization. A chiral aldehyde and a chiral ketone have been probed, with respective achiral controls, on two accessible amine-functionalized MOF substrates. Modifying UiO-66 NH2 with the natural product derived (R)-2,2-dimethyl-1,3-dioxolane-4-carboxaldehyde ((R)-1 aldehyde) was found to be the best-performing combination. The modification was shown to be covalent, chiral, and characteristically proceeding through imine formation. A molecular-level inquiry has furthermore revealed that the modification consists of oligomer-rich structures on the surface. In silico modeling was shown to predict the localization of the modification correctly. Recent advances in the characterization of MOF color were used to track the imine formation. A strategic approach has yielded a new synthetically facile MOF chiralization method. The modification was shown to result in a surface barrier formation. Thereby, restricted diffusion lengths in the solid phase infer good retention of resolving power in ascending van Deemter régimes in chromatography. This makes the yielding material a promising stationary phase candidate for performant chromatographic enantioseparations.},\n\tlanguage = {en},\n\turldate = {2024-01-20},\n\tinstitution = {Chemistry},\n\tauthor = {Novotny, Balázs Álmos and Majumdar, Sauradeep and Ortega-Guerrero, Andres and Jablonka, Kevin Maik and Moubarak, Elias and Gasilova, Natalia and Smit, Berend},\n\tmonth = jan,\n\tyear = {2024},\n\tdoi = {10.26434/chemrxiv-2024-sd1wl},\n}\n\n
\n
\n\n\n
\n Homochiral metal-organic frameworks (MOFs) are exceptional media for heterogenous enantiodifferentation processes. Their rational design, however, is challenging. An available solution for this is moving from constitutive, structure-bearing, homochiral ligands to separating chiral selector and structural roles. This introduces combinatorial modulability, which is the independent change of parts. Such modulability circumvents limitations of designability. Further considerations narrow emphasis to covalent post-synthetic methods. Covalent post-synthetic methods to introduce chirality are reported broadly, yet the diversity of pertaining linkage chemistry is modest. The present work explored the adaptation of imine chemistry for post-synthetic chiralization. A chiral aldehyde and a chiral ketone have been probed, with respective achiral controls, on two accessible amine-functionalized MOF substrates. Modifying UiO-66 NH2 with the natural product derived (R)-2,2-dimethyl-1,3-dioxolane-4-carboxaldehyde ((R)-1 aldehyde) was found to be the best-performing combination. The modification was shown to be covalent, chiral, and characteristically proceeding through imine formation. A molecular-level inquiry has furthermore revealed that the modification consists of oligomer-rich structures on the surface. In silico modeling was shown to predict the localization of the modification correctly. Recent advances in the characterization of MOF color were used to track the imine formation. A strategic approach has yielded a new synthetically facile MOF chiralization method. The modification was shown to result in a surface barrier formation. Thereby, restricted diffusion lengths in the solid phase infer good retention of resolving power in ascending van Deemter régimes in chromatography. This makes the yielding material a promising stationary phase candidate for performant chromatographic enantioseparations.\n
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\n  \n 2023\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n An Ecosystem for Digital Reticular Chemistry.\n \n \n \n \n\n\n \n Jablonka, K. M.; Rosen, A. S.; Krishnapriyan, A. S.; and Smit, B.\n\n\n \n\n\n\n ACS Central Science, 9(4): 563–581. March 2023.\n Publisher: American Chemical Society\n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_ecosystem_2023,\n\ttitle = {An {Ecosystem} for {Digital} {Reticular} {Chemistry}},\n\tvolume = {9},\n\tissn = {2374-7943},\n\turl = {https://doi.org/10.1021/acscentsci.2c01177},\n\tdoi = {10.1021/acscentsci.2c01177},\n\tabstract = {Digital reticular chemistry is rapidly evolving into a pillar of modern chemistry. It is now at a critical junction in which an ecosystem of common data sets, tools, and good practices is needed to prevent this field from becoming an art rather than a science. We present the fundamentals of such an ecosystem and discuss common pitfalls that illustrate its importance.},\n\tnumber = {4},\n\turldate = {2023-03-11},\n\tjournal = {ACS Central Science},\n\tauthor = {Jablonka, Kevin Maik and Rosen, Andrew S. and Krishnapriyan, Aditi S. and Smit, Berend},\n\tmonth = mar,\n\tyear = {2023},\n\tnote = {Publisher: American Chemical Society},\n\tpages = {563--581},\n}\n\n
\n
\n\n\n
\n Digital reticular chemistry is rapidly evolving into a pillar of modern chemistry. It is now at a critical junction in which an ecosystem of common data sets, tools, and good practices is needed to prevent this field from becoming an art rather than a science. We present the fundamentals of such an ecosystem and discuss common pitfalls that illustrate its importance.\n
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\n \n\n \n \n \n \n \n \n 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.\n \n \n \n \n\n\n \n Jablonka, K. M.; Ai, Q.; Al-Feghali, A.; Badhwar, S.; D. Bocarsly, J.; M. Bran, A.; Bringuier, S.; Catherine Brinson, L.; Choudhary, K.; Circi, D.; Cox, S.; Jong, W. A. d.; L. Evans, M.; Gastellu, N.; Genzling, J.; Victoria Gil, M.; K. Gupta, A.; Hong, Z.; Imran, A.; Kruschwitz, S.; Labarre, A.; Lála, J.; Liu, T.; Ma, S.; Majumdar, S.; W. Merz, G.; Moitessier, N.; Moubarak, E.; Mouriño, B.; Pelkie, B.; Pieler, M.; Caldas Ramos, M.; Ranković, B.; G. Rodriques, S.; N. Sanders, J.; Schwaller, P.; Schwarting, M.; Shi, J.; Smit, B.; E. Smith, B.; Herck, J. V.; Völker, C.; Ward, L.; Warren, S.; Weiser, B.; Zhang, S.; Zhang, X.; Ahmad Zia, G.; Scourtas, A.; J. Schmidt, K.; Foster, I.; D. White, A.; and Blaiszik, B.\n\n\n \n\n\n\n Digital Discovery, 2(5): 1233–1250. 2023.\n Publisher: Royal Society of Chemistry\n\n\n\n
\n\n\n\n \n \n \"14Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_14_2023,\n\ttitle = {14 examples of how {LLMs} can transform materials science and chemistry: a reflection on a large language model hackathon},\n\tvolume = {2},\n\tshorttitle = {14 examples of how {LLMs} can transform materials science and chemistry},\n\turl = {https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00113j},\n\tdoi = {10.1039/D3DD00113J},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2023-12-09},\n\tjournal = {Digital Discovery},\n\tauthor = {Jablonka, Kevin Maik and Ai, Qianxiang and Al-Feghali, Alexander and Badhwar, Shruti and D. Bocarsly, Joshua and M. Bran, Andres and Bringuier, Stefan and Catherine Brinson, L. and Choudhary, Kamal and Circi, Defne and Cox, Sam and Jong, Wibe A. de and L. Evans, Matthew and Gastellu, Nicolas and Genzling, Jerome and Victoria Gil, María and K. Gupta, Ankur and Hong, Zhi and Imran, Alishba and Kruschwitz, Sabine and Labarre, Anne and Lála, Jakub and Liu, Tao and Ma, Steven and Majumdar, Sauradeep and W. Merz, Garrett and Moitessier, Nicolas and Moubarak, Elias and Mouriño, Beatriz and Pelkie, Brenden and Pieler, Michael and Caldas Ramos, Mayk and Ranković, Bojana and G. Rodriques, Samuel and N. Sanders, Jacob and Schwaller, Philippe and Schwarting, Marcus and Shi, Jiale and Smit, Berend and E. Smith, Ben and Herck, Joren Van and Völker, Christoph and Ward, Logan and Warren, Sean and Weiser, Benjamin and Zhang, Sylvester and Zhang, Xiaoqi and Ahmad Zia, Ghezal and Scourtas, Aristana and J. Schmidt, K. and Foster, Ian and D. White, Andrew and Blaiszik, Ben},\n\tyear = {2023},\n\tnote = {Publisher: Royal Society of Chemistry},\n\tpages = {1233--1250},\n}\n\n
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\n \n\n \n \n \n \n \n \n In Search of Covalent Organic Framework Photocatalysts: A DFT-Based Screening Approach.\n \n \n \n \n\n\n \n Mourino, B.; Jablonka, K. M.; Ortega-Guerrero, A.; and Smit, B.\n\n\n \n\n\n\n Advanced Functional Materials, 33(32): 2301594. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.202301594\n\n\n\n
\n\n\n\n \n \n \"InPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{mourino_search_2023,\n\ttitle = {In {Search} of {Covalent} {Organic} {Framework} {Photocatalysts}: {A} {DFT}-{Based} {Screening} {Approach}},\n\tvolume = {33},\n\tcopyright = {© 2023 The Authors. Advanced Functional Materials published by Wiley-VCH GmbH},\n\tissn = {1616-3028},\n\tshorttitle = {In {Search} of {Covalent} {Organic} {Framework} {Photocatalysts}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202301594},\n\tdoi = {10.1002/adfm.202301594},\n\tabstract = {Covalent organic frameworks (COFs) stand out as prospective organic-based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge-carrier mobility. The “Clean, Uniform, Refined with Automatic Tracking from Experimental Database” (CURATED) COFs is a database of reported experimental COFs that until now remained mostly unexplored for photocatalysis. In this study, the CURATED COFs database is screened for discovering potential photocatalysts using a set of DFT-based descriptors that cost-effectively assesses visible light absorption, preliminary thermodynamic feasibility of the desired pair of redox reactions, charge separation, and charge-carrier mobility. The workflow can shortlist 13 COFs as prospective candidates for water splitting, and identify materials (Nx-COF (x = 0–3)) that have been reported as candidates for hydrogen evolution reaction. Overall, the strategy addresses the challenge of exploring a large number of COFs by directing future research toward a selective group of COFs, while providing valuable insights into the structural design for achieving a desired photocatalytic process.},\n\tlanguage = {en},\n\tnumber = {32},\n\turldate = {2024-01-20},\n\tjournal = {Advanced Functional Materials},\n\tauthor = {Mourino, Beatriz and Jablonka, Kevin Maik and Ortega-Guerrero, Andres and Smit, Berend},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.202301594},\n\tkeywords = {covalent organic frameworks, density functional theory calculations, photocatalysis},\n\tpages = {2301594},\n}\n\n
\n
\n\n\n
\n Covalent organic frameworks (COFs) stand out as prospective organic-based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge-carrier mobility. The “Clean, Uniform, Refined with Automatic Tracking from Experimental Database” (CURATED) COFs is a database of reported experimental COFs that until now remained mostly unexplored for photocatalysis. In this study, the CURATED COFs database is screened for discovering potential photocatalysts using a set of DFT-based descriptors that cost-effectively assesses visible light absorption, preliminary thermodynamic feasibility of the desired pair of redox reactions, charge separation, and charge-carrier mobility. The workflow can shortlist 13 COFs as prospective candidates for water splitting, and identify materials (Nx-COF (x = 0–3)) that have been reported as candidates for hydrogen evolution reaction. Overall, the strategy addresses the challenge of exploring a large number of COFs by directing future research toward a selective group of COFs, while providing valuable insights into the structural design for achieving a desired photocatalytic process.\n
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\n \n\n \n \n \n \n \n \n Shedding Light on the Stakeholders' Perspectives for Carbon Capture.\n \n \n \n \n\n\n \n Charalambous, C.; Moubarak, E.; Schilling, J.; Fernandez, E. S.; Wang, J.; Herraiz, L.; Mcilwaine, F.; Jablonka, K. M.; Moosavi, S. M.; Herck, J. V.; Mouchaham, G.; Serre, C.; Bardow, A.; Smit, B.; and Garcia, S.\n\n\n \n\n\n\n June 2023.\n \n\n\n\n
\n\n\n\n \n \n \"SheddingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{charalambous_shedding_2023,\n\ttitle = {Shedding {Light} on the {Stakeholders}' {Perspectives} for {Carbon} {Capture}},\n\turl = {https://chemrxiv.org/engage/chemrxiv/article-details/648c0b264f8b1884b76034d9},\n\tdoi = {10.26434/chemrxiv-2023-sn90q},\n\tabstract = {Reducing CO2 emissions requires urgently deploying large-scale carbon capture technologies, amongst other strategies. The quest for optimum technologies is a multi-objective problem involving various stakeholders. Today's research of these technologies follows a sequential approach, with chemists focusing first on material design and engineers subsequently seeking the optimal process. Eventually, this combination of materials and processes operates at a scale that significantly impacts the economy and the environment. Understanding these impacts requires analyzing factors such as greenhouse gas emissions over the lifetime of the capture plant, which now constitutes one of the final steps. In this work, we present the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which seamlessly connects materials, process design, techno-economics, and life-cycle assessment. We compare over sixty case studies in which CO2 is captured from different sources in five world regions with different technologies. These studies illustrate how the platform simultaneously informs all stakeholders: identifying the cheapest technology and optimal process configuration, revealing the molecular characteristics of top-performing materials, determining the best locations, and informing on environmental impacts, co-benefits, and trade-offs. Our platform brings together all stakeholders at an early stage of research, which is essential to accelerate innovations at a time this is most needed.},\n\tlanguage = {en},\n\turldate = {2023-12-09},\n\tpublisher = {ChemRxiv},\n\tauthor = {Charalambous, Charithea and Moubarak, Elias and Schilling, Johannes and Fernandez, Eva Sanchez and Wang, Jin-Yu and Herraiz, Laura and Mcilwaine, Fergus and Jablonka, Kevin Maik and Moosavi, Seyed Mohamad and Herck, Joren Van and Mouchaham, Georges and Serre, Christian and Bardow, André and Smit, Berend and Garcia, Susana},\n\tmonth = jun,\n\tyear = {2023},\n\tkeywords = {Life cycle assessment (LCA), carbon capture, materials screening, metal organic frameworks, techno-economics analysis (TEA)},\n}\n\n
\n
\n\n\n
\n Reducing CO2 emissions requires urgently deploying large-scale carbon capture technologies, amongst other strategies. The quest for optimum technologies is a multi-objective problem involving various stakeholders. Today's research of these technologies follows a sequential approach, with chemists focusing first on material design and engineers subsequently seeking the optimal process. Eventually, this combination of materials and processes operates at a scale that significantly impacts the economy and the environment. Understanding these impacts requires analyzing factors such as greenhouse gas emissions over the lifetime of the capture plant, which now constitutes one of the final steps. In this work, we present the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which seamlessly connects materials, process design, techno-economics, and life-cycle assessment. We compare over sixty case studies in which CO2 is captured from different sources in five world regions with different technologies. These studies illustrate how the platform simultaneously informs all stakeholders: identifying the cheapest technology and optimal process configuration, revealing the molecular characteristics of top-performing materials, determining the best locations, and informing on environmental impacts, co-benefits, and trade-offs. Our platform brings together all stakeholders at an early stage of research, which is essential to accelerate innovations at a time this is most needed.\n
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\n \n\n \n \n \n \n \n \n Biomass to energy: A machine learning model for optimum gasification pathways.\n \n \n \n \n\n\n \n Gil, M. V.; Jablonka, K. M.; García, S.; Pevida, C.; and Smit, B.\n\n\n \n\n\n\n Digital Discovery,10.1039.D3DD00079F. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"BiomassPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{gil_biomass_2023,\n\ttitle = {Biomass to energy: {A} machine learning model for optimum gasification pathways},\n\tissn = {2635-098X},\n\tshorttitle = {Biomass to energy},\n\turl = {http://pubs.rsc.org/en/Content/ArticleLanding/2023/DD/D3DD00079F},\n\tdoi = {10.1039/D3DD00079F},\n\tabstract = {Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, identifying the best gasification route for a given biomass relies on trial and error, which involves time-consuming experimentation that, given the wide range of biomass feedstocks available, slows down the deployment of the technology. Here, we use a supervised non-parametric machine-learning method, Gaussian process regression (GPR), that provides robust predictions when working with small datasets, to develop a model to find the optimal application of a particular biomass in gasification processes.},\n\tlanguage = {en},\n\turldate = {2023-07-18},\n\tjournal = {Digital Discovery},\n\tauthor = {Gil, María Victoria and Jablonka, Kevin Maik and García, Susana and Pevida, Cova and Smit, Berend},\n\tyear = {2023},\n\tpages = {10.1039.D3DD00079F},\n}\n\n
\n
\n\n\n
\n Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, identifying the best gasification route for a given biomass relies on trial and error, which involves time-consuming experimentation that, given the wide range of biomass feedstocks available, slows down the deployment of the technology. Here, we use a supervised non-parametric machine-learning method, Gaussian process regression (GPR), that provides robust predictions when working with small datasets, to develop a model to find the optimal application of a particular biomass in gasification processes.\n
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\n \n\n \n \n \n \n \n \n Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant.\n \n \n \n \n\n\n \n Jablonka, K. M.; Charalambous, C.; Sanchez Fernandez, E.; Wiechers, G.; Monteiro, J.; Moser, P.; Smit, B.; and Garcia, S.\n\n\n \n\n\n\n Science Advances, 9(1): eadc9576. January 2023.\n Publisher: American Association for the Advancement of Science\n\n\n\n
\n\n\n\n \n \n \"MachinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_machine_2023,\n\ttitle = {Machine learning for industrial processes: {Forecasting} amine emissions from a carbon capture plant},\n\tvolume = {9},\n\tshorttitle = {Machine learning for industrial processes},\n\turl = {https://www.science.org/doi/10.1126/sciadv.adc9576},\n\tdoi = {10.1126/sciadv.adc9576},\n\tabstract = {One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent into the atmosphere. To understand how these emissions are affected by the intermittent operation of a power plant, we performed stress tests on a plant operating with a mixture of two amines, 2-amino-2-methyl-1-propanol and piperazine (CESAR1). To forecast the emissions and model the impact of interventions, we developed a machine learning model. Our model showed that some interventions have opposite effects on the emissions of the components of the solvent. Thus, mitigation strategies required for capture plants operating on a single component solvent (e.g., monoethanolamine) need to be reconsidered if operated using a mixture of amines. Amine emissions from a solvent-based carbon capture plant are an example of a process that is too complex to be described by conventional process models. We, therefore, expect that our approach can be more generally applied.},\n\tnumber = {1},\n\turldate = {2023-01-05},\n\tjournal = {Science Advances},\n\tauthor = {Jablonka, Kevin Maik and Charalambous, Charithea and Sanchez Fernandez, Eva and Wiechers, Georg and Monteiro, Juliana and Moser, Peter and Smit, Berend and Garcia, Susana},\n\tmonth = jan,\n\tyear = {2023},\n\tnote = {Publisher: American Association for the Advancement of Science},\n\tpages = {eadc9576},\n}\n\n
\n
\n\n\n
\n One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent into the atmosphere. To understand how these emissions are affected by the intermittent operation of a power plant, we performed stress tests on a plant operating with a mixture of two amines, 2-amino-2-methyl-1-propanol and piperazine (CESAR1). To forecast the emissions and model the impact of interventions, we developed a machine learning model. Our model showed that some interventions have opposite effects on the emissions of the components of the solvent. Thus, mitigation strategies required for capture plants operating on a single component solvent (e.g., monoethanolamine) need to be reconsidered if operated using a mixture of amines. Amine emissions from a solvent-based carbon capture plant are an example of a process that is too complex to be described by conventional process models. We, therefore, expect that our approach can be more generally applied.\n
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\n  \n 2022\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Making the collective knowledge of chemistry open and machine actionable.\n \n \n \n \n\n\n \n Jablonka, K. M.; Patiny, L.; and Smit, B.\n\n\n \n\n\n\n Nature Chemistry, 14(4): 365–376. April 2022.\n \n\n\n\n
\n\n\n\n \n \n \"MakingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{jablonka_making_2022,\n\ttitle = {Making the collective knowledge of chemistry open and machine actionable},\n\tvolume = {14},\n\tcopyright = {2022 Springer Nature Limited},\n\tissn = {1755-4349},\n\turl = {https://www.nature.com/articles/s41557-022-00910-7},\n\tdoi = {10.1038/s41557-022-00910-7},\n\tabstract = {Large amounts of data are generated in chemistry labs—nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist—we only need to connect, polish and embrace them.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2022-07-13},\n\tjournal = {Nature Chemistry},\n\tauthor = {Jablonka, Kevin Maik and Patiny, Luc and Smit, Berend},\n\tmonth = apr,\n\tyear = {2022},\n\tkeywords = {Chemistry, Research data},\n\tpages = {365--376},\n}\n\n
\n
\n\n\n
\n Large amounts of data are generated in chemistry labs—nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist—we only need to connect, polish and embrace them.\n
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\n \n\n \n \n \n \n \n \n Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF.\n \n \n \n \n\n\n \n Domingues, N. P.; Moosavi, S. M.; Talirz, L.; Jablonka, K. M.; Ireland, C. P.; Ebrahim, F. M.; and Smit, B.\n\n\n \n\n\n\n Communications Chemistry, 5(1): 1–8. December 2022.\n Number: 1 Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{domingues_using_2022,\n\ttitle = {Using genetic algorithms to systematically improve the synthesis conditions of {Al}-{PMOF}},\n\tvolume = {5},\n\tcopyright = {2022 The Author(s)},\n\tissn = {2399-3669},\n\turl = {https://www.nature.com/articles/s42004-022-00785-2},\n\tdoi = {10.1038/s42004-022-00785-2},\n\tabstract = {The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80\\% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-12-13},\n\tjournal = {Communications Chemistry},\n\tauthor = {Domingues, Nency P. and Moosavi, Seyed Mohamad and Talirz, Leopold and Jablonka, Kevin Maik and Ireland, Christopher P. and Ebrahim, Fatmah Mish and Smit, Berend},\n\tmonth = dec,\n\tyear = {2022},\n\tnote = {Number: 1\nPublisher: Nature Publishing Group},\n\tkeywords = {Automation, Carbon capture and storage, Metal–organic frameworks, Microwave chemistry},\n\tpages = {1--8},\n}\n\n
\n
\n\n\n
\n The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.\n
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\n \n\n \n \n \n \n \n \n SELFIES and the future of molecular string representations.\n \n \n \n \n\n\n \n Krenn, M.; Ai, Q.; Barthel, S.; Carson, N.; Frei, A.; Frey, N. C.; Friederich, P.; Gaudin, T.; Gayle, A. A.; Jablonka, K. M.; Lameiro, R. F.; Lemm, D.; Lo, A.; Moosavi, S. M.; Nápoles-Duarte, J. M.; Nigam, A.; Pollice, R.; Rajan, K.; Schatzschneider, U.; Schwaller, P.; Skreta, M.; Smit, B.; Strieth-Kalthoff, F.; Sun, C.; Tom, G.; Falk von Rudorff, G.; Wang, A.; White, A. D.; Young, A.; Yu, R.; and Aspuru-Guzik, A.\n\n\n \n\n\n\n Patterns, 3(10): 100588. October 2022.\n \n\n\n\n
\n\n\n\n \n \n \"SELFIESPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{krenn_selfies_2022,\n\ttitle = {{SELFIES} and the future of molecular string representations},\n\tvolume = {3},\n\tissn = {2666-3899},\n\turl = {https://www.sciencedirect.com/science/article/pii/S2666389922002069},\n\tdoi = {10.1016/j.patter.2022.100588},\n\tabstract = {Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\\% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-10-31},\n\tjournal = {Patterns},\n\tauthor = {Krenn, Mario and Ai, Qianxiang and Barthel, Senja and Carson, Nessa and Frei, Angelo and Frey, Nathan C. and Friederich, Pascal and Gaudin, Théophile and Gayle, Alberto Alexander and Jablonka, Kevin Maik and Lameiro, Rafael F. and Lemm, Dominik and Lo, Alston and Moosavi, Seyed Mohamad and Nápoles-Duarte, José Manuel and Nigam, AkshatKumar and Pollice, Robert and Rajan, Kohulan and Schatzschneider, Ulrich and Schwaller, Philippe and Skreta, Marta and Smit, Berend and Strieth-Kalthoff, Felix and Sun, Chong and Tom, Gary and Falk von Rudorff, Guido and Wang, Andrew and White, Andrew D. and Young, Adamo and Yu, Rose and Aspuru-Guzik, Alán},\n\tmonth = oct,\n\tyear = {2022},\n\tpages = {100588},\n}\n\n
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\n Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.\n
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\n \n\n \n \n \n \n \n \n Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks.\n \n \n \n \n\n\n \n Ongari, D.; Talirz, L.; Jablonka, K. M.; Siderius, D. W.; and Smit, B.\n\n\n \n\n\n\n Journal of Chemical & Engineering Data, 67(7): 1743–1756. July 2022.\n Publisher: American Chemical Society\n\n\n\n
\n\n\n\n \n \n \"Data-DrivenPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ongari_data-driven_2022,\n\ttitle = {Data-{Driven} {Matching} of {Experimental} {Crystal} {Structures} and {Gas} {Adsorption} {Isotherms} of {Metal}–{Organic} {Frameworks}},\n\tvolume = {67},\n\tissn = {0021-9568},\n\turl = {https://doi.org/10.1021/acs.jced.1c00958},\n\tdoi = {10.1021/acs.jced.1c00958},\n\tabstract = {Porous metal–organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the high dimensional space of materials science and engineering, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is impeded by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the gas adsorption isotherms of metal–organic frameworks collected in the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials to the corresponding crystal structures in the Cambridge Structural Database. With tens of thousands of isotherms and crystal structures reported to date, an automatic approach is needed to establish this link, which we describe in this paper. As a first application and consistency check, we compare the pore volume measured from low-temperature argon or nitrogen isotherms to the geometrical pore volume computed from the crystal structure. Overall, 545 argon or nitrogen isotherms could be matched to a corresponding crystal structure. We find that the pore volume computed via the two complementary methods shows acceptable agreement only in about 35\\% of these cases. We provide the subset of isotherms measured on these materials as a seed for a future and more complete reference data set for computational studies.},\n\tnumber = {7},\n\turldate = {2022-10-31},\n\tjournal = {Journal of Chemical \\& Engineering Data},\n\tauthor = {Ongari, Daniele and Talirz, Leopold and Jablonka, Kevin Maik and Siderius, Daniel W. and Smit, Berend},\n\tmonth = jul,\n\tyear = {2022},\n\tnote = {Publisher: American Chemical Society},\n\tpages = {1743--1756},\n}\n\n
\n
\n\n\n
\n Porous metal–organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the high dimensional space of materials science and engineering, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is impeded by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the gas adsorption isotherms of metal–organic frameworks collected in the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials to the corresponding crystal structures in the Cambridge Structural Database. With tens of thousands of isotherms and crystal structures reported to date, an automatic approach is needed to establish this link, which we describe in this paper. As a first application and consistency check, we compare the pore volume measured from low-temperature argon or nitrogen isotherms to the geometrical pore volume computed from the crystal structure. Overall, 545 argon or nitrogen isotherms could be matched to a corresponding crystal structure. We find that the pore volume computed via the two complementary methods shows acceptable agreement only in about 35% of these cases. We provide the subset of isotherms measured on these materials as a seed for a future and more complete reference data set for computational studies.\n
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\n \n\n \n \n \n \n \n \n Screening of metal‐organic frameworks for carbon capture based on life‐cycle assessment.\n \n \n \n \n\n\n \n Schilling, J.; Bardow, A.; Sanchez-Fernandez, E.; Charalambous, C.; Garcia, S.; Moubarak, E.; Jablonka, K.; and Smit, B.\n\n\n \n\n\n\n Chemie Ingenieur Technik, 94(9): 1356–1357. September 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ScreeningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{schilling_screening_2022,\n\ttitle = {Screening of metal‐organic frameworks for carbon capture based on life‐cycle assessment},\n\tvolume = {94},\n\tissn = {0009-286X, 1522-2640},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/cite.202255163},\n\tdoi = {10.1002/cite.202255163},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2022-09-05},\n\tjournal = {Chemie Ingenieur Technik},\n\tauthor = {Schilling, J. and Bardow, A. and Sanchez-Fernandez, E. and Charalambous, C. and Garcia, S. and Moubarak, E. and Jablonka, K. and Smit, B.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {1356--1357},\n}\n\n
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\n \n\n \n \n \n \n \n \n xtal2png: A Python package for representing crystalstructure as PNG files.\n \n \n \n \n\n\n \n Baird, S. G.; Jablonka, K. M.; Alverson, M. D.; Sayeed, H. M.; Khan, M. F.; Seegmiller, C.; Smit, B.; and Sparks, T. D.\n\n\n \n\n\n\n Journal of Open Source Software, 7(76): 4528. August 2022.\n \n\n\n\n
\n\n\n\n \n \n \"xtal2png:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{baird_xtal2png_2022,\n\ttitle = {xtal2png: {A} {Python} package for representing crystalstructure as {PNG} files},\n\tvolume = {7},\n\tissn = {2475-9066},\n\tshorttitle = {xtal2png},\n\turl = {https://joss.theoj.org/papers/10.21105/joss.04528},\n\tdoi = {10.21105/joss.04528},\n\tnumber = {76},\n\turldate = {2022-09-05},\n\tjournal = {Journal of Open Source Software},\n\tauthor = {Baird, Sterling G. and Jablonka, Kevin M. and Alverson, Michael D. and Sayeed, Hasan M. and Khan, Mohammed Faris and Seegmiller, Colton and Smit, Berend and Sparks, Taylor D.},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {4528},\n}\n\n
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\n \n\n \n \n \n \n \n \n Making Molecules Vibrate: Interactive Web Environment for the Teaching of Infrared Spectroscopy.\n \n \n \n \n\n\n \n Jablonka, K. M.; Patiny, L.; and Smit, B.\n\n\n \n\n\n\n Journal of Chemical Education, 99(2): 561–569. February 2022.\n \n\n\n\n
\n\n\n\n \n \n \"MakingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_making_2022-1,\n\ttitle = {Making {Molecules} {Vibrate}: {Interactive} {Web} {Environment} for the {Teaching} of {Infrared} {Spectroscopy}},\n\tvolume = {99},\n\tissn = {0021-9584},\n\tshorttitle = {Making {Molecules} {Vibrate}},\n\turl = {https://doi.org/10.1021/acs.jchemed.1c01101},\n\tdoi = {10.1021/acs.jchemed.1c01101},\n\tabstract = {Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique because of its ability to identify functional groups and its ease of use. Interestingly, it allows the capture of electronic effects via their influence on the bond strength of “probes”, such as the carbonyl group and also offers a wealth of examples for discussion on the theory of electronic transitions. For this reason, IR spectroscopy is typically taught both in theoretical classes and in applied structural analysis courses. In practice, there is rarely a link between those courses, and both suffer from the lack of exploratory learning, that is, tools with which students can explore the interplay between symmetry and selection rules, as well as electronic effects and vibrational frequencies─with almost immediate feedback. In practice, this might lead to students that are well skilled in looking up vibrational frequencies in lookup tables but do not understand the links to electronic effects and reactivity. Here, we introduce a web app that leverages semiempirical quantum mechanical (or force-field based) calculations, performed on a web service, in an interactive interface to provide an environment in which students can explore how slight changes to the structure manifest in changes of the spectrum. This approach avoids the time-consuming handling of potentially hazardous materials that might not be readily available and invites students to play with spectroscopy─to “see” and “test” electronic effects that are so commonplace in organic chemistry education. As a “side effect”, our web app also provides a powerful aid for research scientists to investigate how different structural modifications, such as substitution, isomerism, or steric strain, would manifest in the infrared spectrum.},\n\tnumber = {2},\n\turldate = {2022-07-13},\n\tjournal = {Journal of Chemical Education},\n\tauthor = {Jablonka, Kevin Maik and Patiny, Luc and Smit, Berend},\n\tmonth = feb,\n\tyear = {2022},\n\tpages = {561--569},\n}\n\n
\n
\n\n\n
\n Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique because of its ability to identify functional groups and its ease of use. Interestingly, it allows the capture of electronic effects via their influence on the bond strength of “probes”, such as the carbonyl group and also offers a wealth of examples for discussion on the theory of electronic transitions. For this reason, IR spectroscopy is typically taught both in theoretical classes and in applied structural analysis courses. In practice, there is rarely a link between those courses, and both suffer from the lack of exploratory learning, that is, tools with which students can explore the interplay between symmetry and selection rules, as well as electronic effects and vibrational frequencies─with almost immediate feedback. In practice, this might lead to students that are well skilled in looking up vibrational frequencies in lookup tables but do not understand the links to electronic effects and reactivity. Here, we introduce a web app that leverages semiempirical quantum mechanical (or force-field based) calculations, performed on a web service, in an interactive interface to provide an environment in which students can explore how slight changes to the structure manifest in changes of the spectrum. This approach avoids the time-consuming handling of potentially hazardous materials that might not be readily available and invites students to play with spectroscopy─to “see” and “test” electronic effects that are so commonplace in organic chemistry education. As a “side effect”, our web app also provides a powerful aid for research scientists to investigate how different structural modifications, such as substitution, isomerism, or steric strain, would manifest in the infrared spectrum.\n
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\n \n\n \n \n \n \n \n \n Characterization of Chemisorbed Species and Active Adsorption Sites in Mg–Al Mixed Metal Oxides for High-Temperature CO2 Capture.\n \n \n \n \n\n\n \n Lund, A.; Manohara, G. V.; Song, A.; Jablonka, K. M.; Ireland, C. P.; Cheah, L. A.; Smit, B.; Garcia, S.; and Reimer, J. A.\n\n\n \n\n\n\n Chemistry of Materials, 34(9): 3893–3901. May 2022.\n \n\n\n\n
\n\n\n\n \n \n \"CharacterizationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{lund_characterization_2022,\n\ttitle = {Characterization of {Chemisorbed} {Species} and {Active} {Adsorption} {Sites} in {Mg}–{Al} {Mixed} {Metal} {Oxides} for {High}-{Temperature} {CO2} {Capture}},\n\tvolume = {34},\n\tissn = {0897-4756},\n\turl = {https://doi.org/10.1021/acs.chemmater.1c03101},\n\tdoi = {10.1021/acs.chemmater.1c03101},\n\tabstract = {Mg–Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as adsorbents for CO2 capture of industrial plant emissions. To aid in the design and optimization of these materials for CO2 capture at 200 °C, we have used a combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO2 gas sorption products and determine the various sorption sites in Mg–Al MMOs. A comparison of the DFT cluster calculations with the observed 13C chemical shifts of the chemisorbed products indicates that mono- and bidentate carbonates are formed at the Mg–O sites with adjacent Al substitution of an Mg atom, while the bicarbonates are formed at Mg–OH sites without adjacent Al substitution. Quantitative 13C NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al/Mg molar ratios in the MMOs. This detailed understanding of the various basic Mg–O sites presented in MMOs and the formation of the carbonate, bidentate carbonate, and bicarbonate chemisorbed species yields new insights into the mechanism of CO2 adsorption at 200 °C, which can further aid in the design and capture capacity optimization of the materials.},\n\tnumber = {9},\n\turldate = {2022-07-13},\n\tjournal = {Chemistry of Materials},\n\tauthor = {Lund, Alicia and Manohara, G. V. and Song, Ah-Young and Jablonka, Kevin Maik and Ireland, Christopher P. and Cheah, Li Anne and Smit, Berend and Garcia, Susana and Reimer, Jeffrey A.},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {3893--3901},\n}\n\n
\n
\n\n\n
\n Mg–Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as adsorbents for CO2 capture of industrial plant emissions. To aid in the design and optimization of these materials for CO2 capture at 200 °C, we have used a combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO2 gas sorption products and determine the various sorption sites in Mg–Al MMOs. A comparison of the DFT cluster calculations with the observed 13C chemical shifts of the chemisorbed products indicates that mono- and bidentate carbonates are formed at the Mg–O sites with adjacent Al substitution of an Mg atom, while the bicarbonates are formed at Mg–OH sites without adjacent Al substitution. Quantitative 13C NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al/Mg molar ratios in the MMOs. This detailed understanding of the various basic Mg–O sites presented in MMOs and the formation of the carbonate, bidentate carbonate, and bicarbonate chemisorbed species yields new insights into the mechanism of CO2 adsorption at 200 °C, which can further aid in the design and capture capacity optimization of the materials.\n
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\n  \n 2021\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks.\n \n \n \n \n\n\n \n Jablonka, K. M.; Ongari, D.; Moosavi, S. M.; and Smit, B.\n\n\n \n\n\n\n Nature Chemistry, 13(8): 771–777. August 2021.\n Number: 8 Publisher: Nature Publishing Group\n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{jablonka_using_2021,\n\ttitle = {Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks},\n\tvolume = {13},\n\tcopyright = {2021 The Author(s), under exclusive licence to Springer Nature Limited},\n\tissn = {1755-4349},\n\turl = {https://www.nature.com/articles/s41557-021-00717-y},\n\tdoi = {10.1038/s41557-021-00717-y},\n\tabstract = {Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal–organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal–organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2023-02-24},\n\tjournal = {Nature Chemistry},\n\tauthor = {Jablonka, Kevin Maik and Ongari, Daniele and Moosavi, Seyed Mohamad and Smit, Berend},\n\tmonth = aug,\n\tyear = {2021},\n\tnote = {Number: 8\nPublisher: Nature Publishing Group},\n\tkeywords = {Chemistry, Computational chemistry, Metal–organic frameworks},\n\tpages = {771--777},\n}\n\n
\n
\n\n\n
\n Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal–organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal–organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.\n
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\n \n\n \n \n \n \n \n \n A data-driven perspective on the colours of metal–organic frameworks.\n \n \n \n \n\n\n \n Jablonka, K. M.; Moosavi, S. M.; Asgari, M.; Ireland, C.; Patiny, L.; and Smit, B.\n\n\n \n\n\n\n Chemical Science, 12(10): 3587–3598. March 2021.\n Publisher: The Royal Society of Chemistry\n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_data-driven_2021,\n\ttitle = {A data-driven perspective on the colours of metal–organic frameworks},\n\tvolume = {12},\n\tissn = {2041-6539},\n\turl = {https://pubs.rsc.org/en/content/articlelanding/2021/sc/d0sc05337f},\n\tdoi = {10.1039/D0SC05337F},\n\tabstract = {Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-12-01},\n\tjournal = {Chemical Science},\n\tauthor = {Jablonka, Kevin Maik and Moosavi, Seyed Mohamad and Asgari, Mehrdad and Ireland, Christopher and Patiny, Luc and Smit, Berend},\n\tmonth = mar,\n\tyear = {2021},\n\tnote = {Publisher: The Royal Society of Chemistry},\n\tpages = {3587--3598},\n}\n\n
\n
\n\n\n
\n Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.\n
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\n \n\n \n \n \n \n \n \n Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.\n \n \n \n \n\n\n \n Majumdar, S.; Moosavi, S. M.; Jablonka, K. M.; Ongari, D.; and Smit, B.\n\n\n \n\n\n\n ACS Applied Materials & Interfaces, 13(51): 61004–61014. December 2021.\n \n\n\n\n
\n\n\n\n \n \n \"DiversifyingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{majumdar_diversifying_2021,\n\ttitle = {Diversifying {Databases} of {Metal} {Organic} {Frameworks} for {High}-{Throughput} {Computational} {Screening}},\n\tvolume = {13},\n\tissn = {1944-8244},\n\turl = {https://doi.org/10.1021/acsami.1c16220},\n\tdoi = {10.1021/acsami.1c16220},\n\tabstract = {By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.},\n\tnumber = {51},\n\turldate = {2022-07-13},\n\tjournal = {ACS Applied Materials \\& Interfaces},\n\tauthor = {Majumdar, Sauradeep and Moosavi, Seyed Mohamad and Jablonka, Kevin Maik and Ongari, Daniele and Smit, Berend},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {61004--61014},\n}\n\n
\n
\n\n\n
\n By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.\n
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\n \n\n \n \n \n \n \n \n Color-tunable and high quantum-yield luminescence from a biomolecule-inspired single species emitter of white light.\n \n \n \n \n\n\n \n Ebrahim, F. M.; Fumanal, M.; Gladysiak, A.; Kadioglu, O.; Jablonka, K.; Ongari, D.; Mace, A.; Shyshkanov, S.; Saris, S.; Ireland, C.; Dyson, P.; Stylianou, K.; and Smit, B.\n\n\n \n\n\n\n Technical Report Chemistry, August 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Color-tunablePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@techreport{ebrahim_color-tunable_2021,\n\ttype = {preprint},\n\ttitle = {Color-tunable and high quantum-yield luminescence from a biomolecule-inspired single species emitter of white light},\n\turl = {https://chemrxiv.org/engage/chemrxiv/article-details/6107bb0640c8bd68959a2c81},\n\turldate = {2021-10-22},\n\tinstitution = {Chemistry},\n\tauthor = {Ebrahim, Fatmah Mish and Fumanal, Maria and Gladysiak, Andrzej and Kadioglu, Ozge and Jablonka, Kevin and Ongari, Daniele and Mace, Amber and Shyshkanov, Serhii and Saris, Seryio and Ireland, Christopher and Dyson, Paul and Stylianou, Kyriakos and Smit, Berend},\n\tmonth = aug,\n\tyear = {2021},\n}\n\n
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\n \n\n \n \n \n \n \n \n Bias free multiobjective active learning for materials design and discovery.\n \n \n \n \n\n\n \n Jablonka, K. M.; Jothiappan, G. M.; Wang, S.; Smit, B.; and Yoo, B.\n\n\n \n\n\n\n Nature Communications, 12(1): 2312. April 2021.\n \n\n\n\n
\n\n\n\n \n \n \"BiasPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{jablonka_bias_2021,\n\ttitle = {Bias free multiobjective active learning for materials design and discovery},\n\tvolume = {12},\n\tcopyright = {2021 The Author(s)},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-021-22437-0},\n\tdoi = {10.1038/s41467-021-22437-0},\n\tabstract = {The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-07-13},\n\tjournal = {Nature Communications},\n\tauthor = {Jablonka, Kevin Maik and Jothiappan, Giriprasad Melpatti and Wang, Shefang and Smit, Berend and Yoo, Brian},\n\tmonth = apr,\n\tyear = {2021},\n\tkeywords = {Cheminformatics, Coarse-grained models, Polymer chemistry, Theory and computation},\n\tpages = {2312},\n}\n\n
\n
\n\n\n
\n The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.\n
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\n\n\n
\n \n\n \n \n \n \n \n \n A reproducibility study of \"Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space\".\n \n \n \n \n\n\n \n Jablonka, K. M.; Mcilwaine, F.; Garcia, S.; Smit, B.; and Yoo, B.\n\n\n \n\n\n\n Technical Report arXiv:2102.00700, arXiv, February 2021.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{jablonka_reproducibility_2021,\n\ttitle = {A reproducibility study of "{Augmenting} {Genetic} {Algorithms} with {Deep} {Neural} {Networks} for {Exploring} the {Chemical} {Space}"},\n\turl = {http://arxiv.org/abs/2102.00700},\n\tabstract = {Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES representation and also propose an adaptive, neural network-based penalty that is supposed to improve the diversity of the generated molecules. The main claims of the paper are that this GA outperforms other generative techniques (as measured by the penalized logP) and that a neural network-based adaptive penalty increases the diversity of the generated molecules. In this work, we investigated the reproducibility of their claims. Overall, we were able to reproduce comparable results using the SELFIES-based GA, but mostly by exploiting deficiencies of the (easily optimizable) fitness function (i.e., generating long, sulfur containing chains). In addition, we reproduce results showing that the discriminator can be used to bias the generation of molecules to ones that are similar to the reference set. Lastly, we attempted to quantify the evolution of the diversity, understand the influence of some hyperparameters, and propose improvements to the adaptive penalty.},\n\tnumber = {arXiv:2102.00700},\n\turldate = {2022-07-13},\n\tinstitution = {arXiv},\n\tauthor = {Jablonka, Kevin Maik and Mcilwaine, Fergus and Garcia, Susana and Smit, Berend and Yoo, Brian},\n\tmonth = feb,\n\tyear = {2021},\n\tkeywords = {Computer Science - Machine Learning, Condensed Matter - Materials Science},\n}\n\n
\n
\n\n\n
\n Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES representation and also propose an adaptive, neural network-based penalty that is supposed to improve the diversity of the generated molecules. The main claims of the paper are that this GA outperforms other generative techniques (as measured by the penalized logP) and that a neural network-based adaptive penalty increases the diversity of the generated molecules. In this work, we investigated the reproducibility of their claims. Overall, we were able to reproduce comparable results using the SELFIES-based GA, but mostly by exploiting deficiencies of the (easily optimizable) fitness function (i.e., generating long, sulfur containing chains). In addition, we reproduce results showing that the discriminator can be used to bias the generation of molecules to ones that are similar to the reference set. Lastly, we attempted to quantify the evolution of the diversity, understand the influence of some hyperparameters, and propose improvements to the adaptive penalty.\n
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\n \n\n \n \n \n \n \n \n Connecting lab experiments with computer experiments: Making \"routine\" simulations routine.\n \n \n \n \n\n\n \n Jablonka, K. M.; Zasso, M.; Patiny, L.; Marzari, N.; Pizzi, G.; Smit, B.; and Yakutovich, A. V.\n\n\n \n\n\n\n . August 2021.\n \n\n\n\n
\n\n\n\n \n \n \"ConnectingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_connecting_2021,\n\ttitle = {Connecting lab experiments with computer experiments: {Making} "routine" simulations routine},\n\tshorttitle = {Connecting lab experiments with computer experiments},\n\turl = {https://chemrxiv.org/engage/chemrxiv/article-details/610712b5171fc722caba3008},\n\tdoi = {10.26434/chemrxiv-2021-h3381},\n\tabstract = {Nowadays, computer simulations and experiments are closely interlocked. However, the data and analysis workflows are often barred into "silos" of knowledge — even for routine simulations. Here, we show how a typical electronic laboratory notebook (ELN) environment can be seamlessly integrated with a computational modelling infrastructure. We developed a protocol to initiate advanced molecular or atomic simulations directly from an ELN. Such integration ensures that all the relevant sample and experimental data are transferred from the ELN to the modelling infrastructure, and — once the calculations have completed — back to the ELN. The presented protocol works similar to sending out a sample for external characterisation and enables experimentalists to routinely perform "routine" simulations to compare with their experiments while keeping track of the full experiment and simulation provenance. We illustrate our protocol with some examples of geometry optimisation followed by the calculation of adsorption isotherms, but the implementation can be readily generalised to other techniques such as optical absorption or X-ray photoelectron spectroscopy.},\n\tlanguage = {en},\n\turldate = {2022-07-13},\n\tauthor = {Jablonka, Kevin Maik and Zasso, Michaël and Patiny, Luc and Marzari, Nicola and Pizzi, Giovanni and Smit, Berend and Yakutovich, Aliaksandr V.},\n\tmonth = aug,\n\tyear = {2021},\n}\n\n
\n
\n\n\n
\n Nowadays, computer simulations and experiments are closely interlocked. However, the data and analysis workflows are often barred into \"silos\" of knowledge — even for routine simulations. Here, we show how a typical electronic laboratory notebook (ELN) environment can be seamlessly integrated with a computational modelling infrastructure. We developed a protocol to initiate advanced molecular or atomic simulations directly from an ELN. Such integration ensures that all the relevant sample and experimental data are transferred from the ELN to the modelling infrastructure, and — once the calculations have completed — back to the ELN. The presented protocol works similar to sending out a sample for external characterisation and enables experimentalists to routinely perform \"routine\" simulations to compare with their experiments while keeping track of the full experiment and simulation provenance. We illustrate our protocol with some examples of geometry optimisation followed by the calculation of adsorption isotherms, but the implementation can be readily generalised to other techniques such as optical absorption or X-ray photoelectron spectroscopy.\n
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\n  \n 2020\n \n \n (4)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n \n Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.\n \n \n \n \n\n\n \n Jablonka, K. M.; Ongari, D.; Moosavi, S. M.; and Smit, B.\n\n\n \n\n\n\n Chemical Reviews, 120(16): 8066–8129. August 2020.\n Publisher: American Chemical Society\n\n\n\n
\n\n\n\n \n \n \"Big-DataPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_big-data_2020,\n\ttitle = {Big-{Data} {Science} in {Porous} {Materials}: {Materials} {Genomics} and {Machine} {Learning}},\n\tvolume = {120},\n\tissn = {0009-2665},\n\tshorttitle = {Big-{Data} {Science} in {Porous} {Materials}},\n\turl = {https://doi.org/10.1021/acs.chemrev.0c00004},\n\tdoi = {10.1021/acs.chemrev.0c00004},\n\tabstract = {By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.},\n\tnumber = {16},\n\turldate = {2023-02-27},\n\tjournal = {Chemical Reviews},\n\tauthor = {Jablonka, Kevin Maik and Ongari, Daniele and Moosavi, Seyed Mohamad and Smit, Berend},\n\tmonth = aug,\n\tyear = {2020},\n\tnote = {Publisher: American Chemical Society},\n\tpages = {8066--8129},\n}\n\n
\n
\n\n\n
\n By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.\n
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\n \n\n \n \n \n \n \n \n Charge Separation and Charge Carrier Mobility in Photocatalytic Metal-Organic Frameworks.\n \n \n \n \n\n\n \n Fumanal, M.; Ortega-Guerrero, A.; Jablonka, K. M.; Smit, B.; and Tavernelli, I.\n\n\n \n\n\n\n Advanced Functional Materials, 30(49): 2003792. December 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ChargePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{fumanal_charge_2020,\n\ttitle = {Charge {Separation} and {Charge} {Carrier} {Mobility} in {Photocatalytic} {Metal}-{Organic} {Frameworks}},\n\tvolume = {30},\n\tissn = {1616-3028},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/adfm.202003792},\n\tdoi = {10.1002/adfm.202003792},\n\tabstract = {Metal-organic frameworks have great potential to be used as photocatalysts due to their ability to combine photosensitizers with catalytic centers within a porous structure. Charge separation and cha...},\n\tlanguage = {en},\n\tnumber = {49},\n\turldate = {2022-07-13},\n\tjournal = {Advanced Functional Materials},\n\tauthor = {Fumanal, Maria and Ortega-Guerrero, Andres and Jablonka, Kevin Maik and Smit, Berend and Tavernelli, Ivano},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {2003792},\n}\n\n
\n
\n\n\n
\n Metal-organic frameworks have great potential to be used as photocatalysts due to their ability to combine photosensitizers with catalytic centers within a porous structure. Charge separation and cha...\n
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\n \n\n \n \n \n \n \n \n Understanding the diversity of the metal-organic framework ecosystem.\n \n \n \n \n\n\n \n Moosavi, S. M.; Nandy, A.; Jablonka, K. M.; Ongari, D.; Janet, J. P.; Boyd, P. G.; Lee, Y.; Smit, B.; and Kulik, H. J.\n\n\n \n\n\n\n Nature Communications, 11(1): 4068. August 2020.\n \n\n\n\n
\n\n\n\n \n \n \"UnderstandingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{moosavi_understanding_2020,\n\ttitle = {Understanding the diversity of the metal-organic framework ecosystem},\n\tvolume = {11},\n\tcopyright = {2020 The Author(s)},\n\tissn = {2041-1723},\n\turl = {https://www.nature.com/articles/s41467-020-17755-8%C2%BB},\n\tdoi = {10.1038/s41467-020-17755-8},\n\tabstract = {Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-07-13},\n\tjournal = {Nature Communications},\n\tauthor = {Moosavi, Seyed Mohamad and Nandy, Aditya and Jablonka, Kevin Maik and Ongari, Daniele and Janet, Jon Paul and Boyd, Peter G. and Lee, Yongjin and Smit, Berend and Kulik, Heather J.},\n\tmonth = aug,\n\tyear = {2020},\n\tkeywords = {Chemistry, Materials for energy and catalysis, Metal–organic frameworks, Theory and computation},\n\tpages = {4068},\n}\n\n
\n
\n\n\n
\n Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.\n
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\n \n\n \n \n \n \n \n \n The Role of Machine Learning in the Understanding and Design of Materials.\n \n \n \n \n\n\n \n Moosavi, S. M.; Jablonka, K. M.; and Smit, B.\n\n\n \n\n\n\n Journal of the American Chemical Society, 142(48): 20273–20287. December 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{moosavi_role_2020,\n\ttitle = {The {Role} of {Machine} {Learning} in the {Understanding} and {Design} of {Materials}},\n\tvolume = {142},\n\tissn = {0002-7863, 1520-5126},\n\turl = {https://pubs.acs.org/doi/10.1021/jacs.0c09105},\n\tdoi = {10.1021/jacs.0c09105},\n\tlanguage = {en},\n\tnumber = {48},\n\turldate = {2020-12-12},\n\tjournal = {Journal of the American Chemical Society},\n\tauthor = {Moosavi, Seyed Mohamad and Jablonka, Kevin Maik and Smit, Berend},\n\tmonth = dec,\n\tyear = {2020},\n\tpages = {20273--20287},\n}\n
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\n
\n  \n 2019\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Applicability of Tail Corrections in the Molecular Simulations of Porous Materials.\n \n \n \n \n\n\n \n Jablonka, K. M.; Ongari, D.; and Smit, B.\n\n\n \n\n\n\n Journal of Chemical Theory and Computation, 15(10): 5635–5641. October 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ApplicabilityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jablonka_applicability_2019,\n\ttitle = {Applicability of {Tail} {Corrections} in the {Molecular} {Simulations} of {Porous} {Materials}},\n\tvolume = {15},\n\tissn = {1549-9618},\n\turl = {https://doi.org/10.1021/acs.jctc.9b00586},\n\tdoi = {10.1021/acs.jctc.9b00586},\n\tabstract = {Molecular simulations with periodic boundary conditions require the definition of a certain cutoff radius, rc, beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases the use of tail corrections is well-established, which can remedy this truncation of the potential. These corrections are built under the assumption that beyond rc the radial distribution function, g(r), is equal to one. In this work we shed some light on the discussion of whether tail corrections should be used in the modeling of heterogeneous systems. We show that for the adsorption of gases in a diverse set of nanoporous crystalline materials (zeolites, covalent organic frameworks, and metal–organic frameworks), tail corrections are a convenient choice to make the adsorption results less sensitive to the details of the truncation.},\n\tnumber = {10},\n\turldate = {2022-07-13},\n\tjournal = {Journal of Chemical Theory and Computation},\n\tauthor = {Jablonka, Kevin Maik and Ongari, Daniele and Smit, Berend},\n\tmonth = oct,\n\tyear = {2019},\n\tpages = {5635--5641},\n}\n\n
\n
\n\n\n
\n Molecular simulations with periodic boundary conditions require the definition of a certain cutoff radius, rc, beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases the use of tail corrections is well-established, which can remedy this truncation of the potential. These corrections are built under the assumption that beyond rc the radial distribution function, g(r), is equal to one. In this work we shed some light on the discussion of whether tail corrections should be used in the modeling of heterogeneous systems. We show that for the adsorption of gases in a diverse set of nanoporous crystalline materials (zeolites, covalent organic frameworks, and metal–organic frameworks), tail corrections are a convenient choice to make the adsorption results less sensitive to the details of the truncation.\n
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\n  \n 2017\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Grundlagen der Thermodynamik für Studierende der Chemie.\n \n \n \n \n\n\n \n Jablonka, K. M.\n\n\n \n\n\n\n of essentialsSpringer Fachmedien Wiesbaden, Wiesbaden, 2017.\n \n\n\n\n
\n\n\n\n \n \n \"GrundlagenPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@book{jablonka_grundlagen_2017,\n\taddress = {Wiesbaden},\n\tseries = {essentials},\n\ttitle = {Grundlagen der {Thermodynamik} für {Studierende} der {Chemie}},\n\tisbn = {978-3-658-17020-2 978-3-658-17021-9},\n\turl = {http://link.springer.com/10.1007/978-3-658-17021-9},\n\turldate = {2022-07-13},\n\tpublisher = {Springer Fachmedien Wiesbaden},\n\tauthor = {Jablonka, Kevin Maik},\n\tyear = {2017},\n}\n\n
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\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Vorrichtung zur katalytischen, photochemischen Aufspaltung von Wasser zur Gewinnung von Wasserstoff.\n \n \n \n \n\n\n \n Jablonka, K.\n\n\n \n\n\n\n July 2015.\n \n\n\n\n
\n\n\n\n \n \n \"VorrichtungPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@patent{jablonka_vorrichtung_2015,\n\ttitle = {Vorrichtung zur katalytischen, photochemischen {Aufspaltung} von {Wasser} zur {Gewinnung} von {Wasserstoff}},\n\turl = {https://patents.google.com/patent/DE102014000888A1/de},\n\tabstract = {Es wird eine Vorrichtung zur katalytischen, photochemischen Aufspaltung von Wasser zur Gewinnung von Wasserstoff angegeben, bei welcher der aus graphitischem Kohlenstoffnitrid bestehende Katalysator an Fasern oder Tröpfchen eines Trägerpolymers angebunden ist und mit diesen zusammen eine makroskopisch zusammenhängende Struktur bildet. Die Fasern- bzw. Tröpfchen haben dabei eine Dicke bzw. einen Durchmesser im Nano- bis Mikrometerbereich. Bevorzugt wird die makroskopisch zusammenhängende Struktur mit einem sogenannten Electrospinning/spraying-Verfahren hergestellt.},\n\tnationality = {DE},\n\tlanguage = {de},\n\tassignee = {JABLONKA JOSEF und DANUTA ALS GESETZLICHE VERTRETER DES MINDERJAHRIGEN JABLONKA},\n\tnumber = {DE102014000888A1},\n\turldate = {2022-07-13},\n\tauthor = {Jablonka, Kevin},\n\tmonth = jul,\n\tyear = {2015},\n\tkeywords = {carbon nitride, electrospinning, fibers, graphitic, polymer},\n}\n\n
\n
\n\n\n
\n Es wird eine Vorrichtung zur katalytischen, photochemischen Aufspaltung von Wasser zur Gewinnung von Wasserstoff angegeben, bei welcher der aus graphitischem Kohlenstoffnitrid bestehende Katalysator an Fasern oder Tröpfchen eines Trägerpolymers angebunden ist und mit diesen zusammen eine makroskopisch zusammenhängende Struktur bildet. Die Fasern- bzw. Tröpfchen haben dabei eine Dicke bzw. einen Durchmesser im Nano- bis Mikrometerbereich. Bevorzugt wird die makroskopisch zusammenhängende Struktur mit einem sogenannten Electrospinning/spraying-Verfahren hergestellt.\n
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