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\n \n\n \n \n \n \n \n \n Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.\n \n \n \n \n\n\n \n Uceda, R. G.; Míguez-Lago, S.; Cruz, C. M.; Pérez-Cañedo, B.; Gijón, A.; Álvarez de Cienfuegos, L.; Mota, A. J.; Miguel, D.; and Cuerva, J. M.\n\n\n \n\n\n\n Advanced Science, n/a(n/a): e74715. 2026.\n _eprint: https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.74715\n\n\n\n
\n\n\n\n \n \n $\"Artificial$ 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 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

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@article{uceda_artificial_2026,\n\ttitle = {Artificial {Intelligence} {Predictions} in {Huge} {Chemical} {Spaces}: {Chiroptical} {Properties} of [6]-helicene {Family}},\n\tvolume = {n/a},\n\tcopyright = {All rights reserved},\n\tissn = {2198-3844},\n\tshorttitle = {Artificial {Intelligence} {Predictions} in {Huge} {Chemical} {Spaces}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/advs.74715},\n\tdoi = {10.1002/advs.74715},\n\tabstract = {Navigating the vast chemical space remains a major challenge in the rational design of materials with tailored properties. Here, we investigate how the properties of the [6]helicene family can be effectively modelled using a local, data-driven AI framework. By predicting each molecule from its closest structural neighbours, we accurately estimate diverse photophysical and (chir)optical properties. The coupling with genetic algorithms enables efficient inverse design and multi-objective optimization, yielding molecules unlikely to arise from intuition alone. The method uncovers [6]helicenes with enhanced electronic circular dichroism (ECD) features, tuned low-energy transitions, and exceptionally large g values, while revealing clear structure–property relationships that translate into practical design rules. Overall, this framework offers a general and efficient route for goal-directed molecular discovery across extensive chemical spaces.},\n\tlanguage = {en},\n\tnumber = {n/a},\n\turldate = {2026-04-24},\n\tjournal = {Advanced Science},\n\tauthor = {Uceda, Rafael G. and Míguez-Lago, Sandra and Cruz, Carlos M. and Pérez-Cañedo, Boris and Gijón, Alfonso and Álvarez de Cienfuegos, Luis and Mota, Antonio J. and Miguel, Delia and Cuerva, Juan M.},\n\tyear = {2026},\n\tnote = {\\_eprint: https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.74715},\n\tkeywords = {6helicenes, artificial intelligence, chiroptical properties, computational chemistry, helical structures},\n\tpages = {e74715},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/T6QCV2VY/Uceda et al. - 2026 - Artificial Intelligence Predictions in Huge Chemical Spaces Chiroptical Properties of [6]-helicene.pdf:application/pdf;Snapshot:/home/alfonso/snap/zotero-snap/common/Zotero/storage/N8TF3YJI/advs.html:text/html},\n}\n\n

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\n \n\n \n \n \n \n \n \n Advancing green hydrogen: next-generation AEMs with reduced hydrogen crossover.\n \n \n \n \n\n\n \n Herranz, D.; Mazo-Sevillano, P. d.; Rodríguez, S.; Gijón, A.; Moreno, J. R. A.; and Ocón, P.\n\n\n \n\n\n\n Energy Materials, 6(1): N/A–N/A. January 2026.\n \n\n\n\n
\n\n\n\n \n \n $\"Advancing$ 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 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

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@article{herranz_advancing_2026,\n\ttitle = {Advancing green hydrogen: next-generation {AEMs} with reduced hydrogen crossover},\n\tvolume = {6},\n\tcopyright = {All rights reserved},\n\tissn = {ISSN 2770-5900},\n\tshorttitle = {Advancing green hydrogen},\n\turl = {https://www.oaepublish.com/articles/energymater.2025.142},\n\tdoi = {10.20517/energymater.2025.142},\n\tabstract = {This study reports the synthesis and characterization of anion exchange membranes (AEMs) tailored for application in alkaline water electrolysis for green hydrogen production. Novel membranes were developed by crosslinking polybenzimidazole (PBI) and poly(vinylbenzyl chloride) (PVBC) in a 1:2 ratio, followed by quaternization with either 1,4-diazabicyclo[2.2.2]octane (DABCO) or 1-methylpyrrolidine (MPY). Their performance was benchmarked against commercial membranes, including Fumasep® FAA-3-50 and Dapozol M-40. The membranes were thoroughly characterized by scanning electron microscopy with energy-dispersive X-ray spectroscopy, infrared and Raman spectroscopies, ionic conductivity, ion exchange capacity, water uptake and swelling measurements. Additionally, molecular dynamics simulations were performed to determine the diffusion coefficients of OH- and H2, providing further insight into ion transport and gas permeability at the molecular level. Electrochemical performance was evaluated in a flow-cell configuration under different pretreatment protocols. A key result of this work is the superior gas-barrier performance of the synthesized membranes. In stability electrolysis tests, both DABCO- and MPY-based membranes showed significantly reduced hydrogen crossover, 36\\% lower than FAA-3-50, decreasing from approximately 2.7\\% to just 1.7\\% H2 detected at the anode. This reduction in crossover is critical for enhancing efficiency and safety in hydrogen production. While FAA-3-50 delivered the best overall performance in short test activation conditions, the synthesized membranes demonstrated highly competitive performance and notable improvements in selectivity and stability. Dapozol M-40 was excluded from further analysis due to its poor electrochemical performance. These findings confirm the potential of tailored PBI/PVBC-based membranes for advanced alkaline electrolysis applications.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2026-04-24},\n\tjournal = {Energy Materials},\n\tpublisher = {OAE Publishing Inc.},\n\tauthor = {Herranz, Daniel and Mazo-Sevillano, Pablo del and Rodríguez, Sara and Gijón, Alfonso and Moreno, Juan Ramón Avilés and Ocón, Pilar},\n\tmonth = jan,\n\tyear = {2026},\n\tpages = {N/A--N/A},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/VDCV8V3E/Herranz et al. - 2026 - Advancing green hydrogen next-generation AEMs with reduced hydrogen crossover.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification.\n \n \n \n \n\n\n \n Gijón, A.; Pujana-Goitia, A.; Perea, E.; Molina-Solana, M.; and Gómez-Romero, J.\n\n\n \n\n\n\n Engineering Applications of Artificial Intelligence, 164: 113331. January 2026.\n \n\n\n\n
\n\n\n\n \n \n $\"Prediction$ 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 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

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@article{gijon_prediction_2026,\n\ttitle = {Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification},\n\tvolume = {164},\n\tcopyright = {All rights reserved},\n\tissn = {0952-1976},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0952197625033627},\n\tdoi = {10.1016/j.engappai.2025.113331},\n\tabstract = {The ever-growing use of wind energy requires the optimization of turbine operations through pitch angle controllers and early fault detection. Accurate and robust models that replicate turbine behavior are essential, particularly for predicting generated power from wind speed. Existing empirical and physics-based models often fail to capture the complex relationships between input variables and power output, aggravated by wind variability. Data-driven methods offer promising alternatives by improving model accuracy and scalability with large datasets. In this study, we use physics-informed neural networks to model historical data from four turbines in a wind farm, embedding physical constraints into the learning process. The proposed models predict power, torque, and power coefficient with high accuracy for both the data and the governing physical laws. Notably, neural networks improve the prediction of the power coefficient by an order of magnitude over empirical models. Physics-informed neural networks also show higher robustness than standard networks under limited data, maintaining accuracy even when trained on reduced datasets. Finally, the inclusion of an evidential layer provides uncertainty estimations that align with absolute errors and allow confidence intervals on the power curve, ensuring consistency with both observed data and manufacturer specifications.},\n\turldate = {2026-04-24},\n\tjournal = {Engineering Applications of Artificial Intelligence},\n\tauthor = {Gijón, Alfonso and Pujana-Goitia, Ainhoa and Perea, Eugenio and Molina-Solana, Miguel and Gómez-Romero, Juan},\n\tmonth = jan,\n\tyear = {2026},\n\tkeywords = {Physics-informed neural networks, Power prediction, Uncertainty quantification, Wind energy, Wind turbine modeling},\n\tpages = {113331},\n\tfile = {ScienceDirect Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/75T2NPIY/Gijón et al. - 2026 - Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty q.pdf:application/pdf;ScienceDirect Snapshot:/home/alfonso/snap/zotero-snap/common/Zotero/storage/VXZGSQI6/S0952197625033627.html:text/html},\n}\n\n

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\n \n\n \n \n \n \n \n \n Experimental application of a semi-parametric model for interpretable and accurate egression analysis of building energy consumption.\n \n \n \n \n\n\n \n Eiraudo, S.; Gijón, A.; Manjavacas, A.; Schiera, D. S.; Barbierato, L.; Molina-Solana, M.; Gómez-Romero, J.; Giannantonio, R.; Bottaccioli, L.; and Lanzini, A.\n\n\n \n\n\n\n Energy and Buildings, 349: 116495. December 2025.\n \n\n\n\n
\n\n\n\n \n \n $\"Experimental$ 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 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\n

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@article{eiraudo_experimental_2025,\n\ttitle = {Experimental application of a semi-parametric model for interpretable and accurate egression analysis of building energy consumption},\n\tvolume = {349},\n\tcopyright = {All rights reserved},\n\tissn = {0378-7788},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0378778825012253},\n\tdoi = {10.1016/j.enbuild.2025.116495},\n\tabstract = {Regression analysis is a versatile tool with numerous applications across diverse domains. Its utility extends to several tasks, including forecasting, inverse modeling, anomaly detection, and pattern identification. Over the years, researchers have mainly focused on two regression categories: parametric and non-parametric analysis. In light of the benefits and drawbacks of both methods, this work introduces a semi-parametric approach, combining regression accuracy and interpretability. This is achieved by designing a hybrid model, that includes a physics-based sub-model and a neural network. The proposed data-driven pipeline is applied to a relevant case study from the energy sector, namely the analysis of building energy consumption, achieving high accuracy compared to the parametric approach. Results demonstrate an increase in the mean coefficient of determination, from 0.77 to 0.94, with a MAPE drop from 5.5 \\% to 2.2 \\%. Meanwhile, the semi-parametric model allows the assessment of the thermal behavior of the buildings, thereby offering an improvement over black-box approaches.},\n\turldate = {2026-04-24},\n\tjournal = {Energy and Buildings},\n\tauthor = {Eiraudo, Simone and Gijón, Alfonso and Manjavacas, Antonio and Schiera, Daniele Salvatore and Barbierato, Luca and Molina-Solana, Miguel and Gómez-Romero, Juan and Giannantonio, Roberta and Bottaccioli, Lorenzo and Lanzini, Andrea},\n\tmonth = dec,\n\tyear = {2025},\n\tkeywords = {Building consumption, Energy, Hybrid models, Interpretable machine-learning, Regression analysis, Semi-parametric models},\n\tpages = {116495},\n}\n\n

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\n \n\n \n \n \n \n \n \n Integrating physics and data-driven approaches: An explainable and uncertainty-aware hybrid model for wind turbine power prediction.\n \n \n \n \n\n\n \n Gijón, A.; Eiraudo, S.; Manjavacas, A.; Schiera, D. S.; Molina-Solana, M.; and Gómez-Romero, J.\n\n\n \n\n\n\n Computer Physics Communications, 316: 109761. November 2025.\n \n\n\n\n
\n\n\n\n \n \n $\"Integrating$ 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 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

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@article{gijon_integrating_2025,\n\ttitle = {Integrating physics and data-driven approaches: {An} explainable and uncertainty-aware hybrid model for wind turbine power prediction},\n\tvolume = {316},\n\tcopyright = {All rights reserved},\n\tissn = {0010-4655},\n\tshorttitle = {Integrating physics and data-driven approaches},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0010465525002632},\n\tdoi = {10.1016/j.cpc.2025.109761},\n\tabstract = {The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37\\% improvement in prediction accuracy over the physics-based model and performs comparably to a purely data-driven reference model, while offering additional advantages in terms of explainability and robustness. To further enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel's output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled phenomena on wind turbine power generation, offering a basis for potential optimization.},\n\turldate = {2026-04-24},\n\tjournal = {Computer Physics Communications},\n\tauthor = {Gijón, Alfonso and Eiraudo, Simone and Manjavacas, Antonio and Schiera, Daniele Salvatore and Molina-Solana, Miguel and Gómez-Romero, Juan},\n\tmonth = nov,\n\tyear = {2025},\n\tkeywords = {Uncertainty quantification, Hybrid models, Explainability, Wind turbines},\n\tpages = {109761},\n}\n\n

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\n \n\n \n \n \n \n \n \n STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG.\n \n \n \n \n\n\n \n Fernández-Martín, R.; Gijón, A.; Feys, O.; Juvené, E.; Aeby, A.; Urbain, C.; De Tiège, X.; and Wens, V.\n\n\n \n\n\n\n Scientific Reports, 15(1): 21017. July 2025.\n \n\n\n\n
\n\n\n\n \n \n $\"STIED:$ 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 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

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@article{fernandez-martin_stied_2025,\n\ttitle = {{STIED}: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with {MEG}},\n\tvolume = {15},\n\tcopyright = {All rights reserved},\n\tissn = {2045-2322},\n\tshorttitle = {{STIED}},\n\turl = {https://www.nature.com/articles/s41598-025-03880-1},\n\tdoi = {10.1038/s41598-025-03880-1},\n\tabstract = {Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. Still, the rise of deep learning (DL)—with its ability to reproduce human-like abilities—could revolutionize clinical MEG practice. Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. Our DL model enabled a successful identification of IEDs in patients suffering from focal epilepsy with frequent and high amplitude spikes (FE group), with high-performance metrics—accuracy, specificity, and sensitivity all exceeding 85\\%—when learning from spatiotemporal features of IEDs. This performance can be attributed to our handling of input data, which mimics established clinical MEG practice. Reverse engineering further revealed that STIED encodes fine spatiotemporal features of IEDs rather than their mere amplitude. The model trained on the FE group also showed promising results when applied to a separate group of presurgical patients with different types of refractory focal epilepsy, though further work is needed to distinguish IEDs from physiological transients. This proof-of-concept study represents a first step towards the use of STIED and DL algorithms in the routine clinical MEG evaluation of epilepsy.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2026-04-24},\n\tjournal = {Scientific Reports},\n\tpublisher = {Nature Publishing Group},\n\tauthor = {Fernández-Martín, Raquel and Gijón, Alfonso and Feys, Odile and Juvené, Elodie and Aeby, Alec and Urbain, Charline and De Tiège, Xavier and Wens, Vincent},\n\tmonth = jul,\n\tyear = {2025},\n\tkeywords = {Epilepsy, Learning algorithms, Network models},\n\tpages = {21017},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/DNBT4ZBJ/Fernández-Martín et al. - 2025 - STIED a deep learning model for the spatiotemporal detection of focal interictal epileptiform disch.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Chiral Intelligence: The Artificial Intelligence-Driven Future of Chiroptical Properties.\n \n \n \n \n\n\n \n Uceda, R. G.; Gijón, A.; Míguez-Lago, S.; Cruz, C. M.; Álvarez de Cienfuegos, L.; Mota, A. J.; Miguel, D.; and Cuerva, J. M.\n\n\n \n\n\n\n ChemPhotoChem, 9(7): e202500079. 2025.\n _eprint: https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cptc.202500079\n\n\n\n
\n\n\n\n \n \n $\"Chiral$ 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 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

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@article{uceda_chiral_2025,\n\ttitle = {Chiral {Intelligence}: {The} {Artificial} {Intelligence}-{Driven} {Future} of {Chiroptical} {Properties}},\n\tvolume = {9},\n\tcopyright = {All rights reserved},\n\tissn = {2367-0932},\n\tshorttitle = {Chiral {Intelligence}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cptc.202500079},\n\tdoi = {10.1002/cptc.202500079},\n\tabstract = {Chirality plays a fundamental role in molecular sciences, with chiroptical properties offering valuable insights into the interaction between chiral molecules and polarized light. Designing chiral materials with enhanced properties requires a deep understanding of underlying physical principles, often revealed only through large datasets. In this context, artificial intelligence (AI) emerges as a powerful tool for accelerating discovery and optimization, efficiently exploring vast chemical spaces. This work explores the synergy between AI and chiroptical properties, highlighting recent advances in data-driven approaches for circular dichroism and circularly polarized luminescence. AI has demonstrated its ability to predict these phenomena accurately while uncovering structure–property relationships that can remain hidden under traditional methods. Various strategies are examined for integrating AI into chiroptical properties and the challenges and future directions of this field are discussed. In conclusion, combining chemical intuition with AI offers great potential for the rational design of next-generation chiral materials. This integration not only promises to unlock novel compounds with enhanced chiroptical properties but also provides new opportunities to deepen our understanding of chiroptical phenomena.},\n\tlanguage = {en},\n\tnumber = {7},\n\turldate = {2026-04-24},\n\tjournal = {ChemPhotoChem},\n\tauthor = {Uceda, Rafael G. and Gijón, Alfonso and Míguez-Lago, Sandra and Cruz, Carlos M. and Álvarez de Cienfuegos, Luis and Mota, Antonio J. and Miguel, Delia and Cuerva, Juan M.},\n\tyear = {2025},\n\tnote = {\\_eprint: https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cptc.202500079},\n\tkeywords = {artificial intelligence, chiroptical properties, circular dichroism, circularly polarized luminescence, molecular designs},\n\tpages = {e202500079},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/3CSRPGMY/Uceda et al. - 2025 - Chiral Intelligence The Artificial Intelligence-Driven Future of Chiroptical Properties.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.\n \n \n \n \n\n\n \n Uceda, R. G.; Gijón, A.; Míguez-Lago, S.; Cruz, C. M.; Blanco, V.; Fernández-Álvarez, F.; Álvarez de Cienfuegos, L.; Molina-Solana, M.; Gómez-Romero, J.; Miguel, D.; Mota, A. J.; and Cuerva, J. M.\n\n\n \n\n\n\n Angewandte Chemie International Edition, 63(49): e202409998. 2024.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.202409998\n\n\n\n
\n\n\n\n \n \n $\"Can$ 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 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

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@article{uceda_can_2024,\n\ttitle = {Can {Deep} {Learning} {Search} for {Exceptional} {Chiroptical} {Properties}? {The} {Halogenated} [6]{Helicene} {Case}},\n\tvolume = {63},\n\tcopyright = {All rights reserved},\n\tissn = {1521-3773},\n\tshorttitle = {Can {Deep} {Learning} {Search} for {Exceptional} {Chiroptical} {Properties}?},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.202409998},\n\tdoi = {10.1002/anie.202409998},\n\tabstract = {The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial-error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the Rmax for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted Rmax obtaining excellent correlation among the values obtained experimentally and the predicted ones.},\n\tlanguage = {en},\n\tnumber = {49},\n\turldate = {2026-04-24},\n\tjournal = {Angewandte Chemie International Edition},\n\tauthor = {Uceda, Rafael G. and Gijón, Alfonso and Míguez-Lago, Sandra and Cruz, Carlos M. and Blanco, Víctor and Fernández-Álvarez, Fátima and Álvarez de Cienfuegos, Luis and Molina-Solana, Miguel and Gómez-Romero, Juan and Miguel, Delia and Mota, Antonio J. and Cuerva, Juan M.},\n\tyear = {2024},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.202409998},\n\tkeywords = {chiroptical properties, 6helicene, deep learning, DFT calculations, rotatory strength},\n\tpages = {e202409998},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/UUUGHN9K/Uceda et al. - 2024 - Can Deep Learning Search for Exceptional Chiroptical Properties The Halogenated [6]Helicene Case.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Graph-neural-network potential energy surface to speed up Monte Carlo simulations of water cluster anions.\n \n \n \n \n\n\n \n Gijón, A.; Molina-Solana, M.; and Gómez-Romero, J.\n\n\n \n\n\n\n Journal of Computational Science, 81: 102383. September 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Graph-neural-network$ 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 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

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@article{gijon_graph-neural-network_2024,\n\ttitle = {Graph-neural-network potential energy surface to speed up {Monte} {Carlo} simulations of water cluster anions},\n\tvolume = {81},\n\tcopyright = {All rights reserved},\n\tissn = {1877-7503},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1877750324001765},\n\tdoi = {10.1016/j.jocs.2024.102383},\n\tabstract = {Regression of potential energy functions stands as one of the most prevalent applications of machine learning in the realm of materials simulation, offering the prospect of accelerating simulations by several orders of magnitude. Recently, graph-based architectures have emerged as particularly adept for modeling molecular systems. However, the development of robust and transferable potentials, leading to stable simulations for different sizes and physical conditions, remains an ongoing area of investigation. In this study, we compare the performance of several graph neural networks for predicting the energy of water cluster anions, a system of fundamental interest in Chemistry and Biology. Following the identification of the graph attention network as the optimal aggregation procedure for this task, we obtained an efficient and accurate energy model. This model is then employed to conduct Monte Carlo simulations of clusters across different sizes, demonstrating stable behavior. Notably, the predicted surface-to-interior state transition point and the bulk energy of the system are consistent with findings from other investigations, at a computational cost three-orders of magnitude lower.},\n\turldate = {2026-04-24},\n\tjournal = {Journal of Computational Science},\n\tauthor = {Gijón, Alfonso and Molina-Solana, Miguel and Gómez-Romero, Juan},\n\tmonth = sep,\n\tyear = {2024},\n\tkeywords = {Atomic potential energy surfaces, Graph neural networks, Monte Carlo simulations, Water cluster anions},\n\tpages = {102383},\n}\n\n

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\n \n\n \n \n \n \n \n \n Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines \\textbar Springer Nature Link.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n 2024.\n \n\n\n\n
\n\n\n\n \n \n $\"Explainable$ Paper\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

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@misc{noauthor_explainable_2024,\n\ttitle = {Explainable {Hybrid} {Semi}-parametric {Model} for {Prediction} of {Power} {Generated} by {Wind} {Turbines} {\\textbar} {Springer} {Nature} {Link}},\n\tcopyright = {All rights reserved},\n\turl = {https://link.springer.com/chapter/10.1007/978-3-031-63775-9_21},\n\turldate = {2026-04-24},\n\tyear = {2024},\n}\n\n

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\n \n 2023\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n Bibliometric analysis of the global scientific production on machine learning applied to different cancer types.\n \n \n \n \n\n\n \n Ruiz-Fresneda, M. A.; Gijón, A.; and Morales-Álvarez, P.\n\n\n \n\n\n\n Environmental Science and Pollution Research, 30(42): 96125–96137. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Bibliometric$ 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 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

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@article{ruiz-fresneda_bibliometric_2023,\n\ttitle = {Bibliometric analysis of the global scientific production on machine learning applied to different cancer types},\n\tvolume = {30},\n\tcopyright = {All rights reserved},\n\tissn = {1614-7499},\n\turl = {https://doi.org/10.1007/s11356-023-28576-9},\n\tdoi = {10.1007/s11356-023-28576-9},\n\tabstract = {Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78\\% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.},\n\tlanguage = {en},\n\tnumber = {42},\n\turldate = {2026-04-24},\n\tjournal = {Environmental Science and Pollution Research},\n\tauthor = {Ruiz-Fresneda, Miguel Angel and Gijón, Alfonso and Morales-Álvarez, Pablo},\n\tmonth = sep,\n\tyear = {2023},\n\tkeywords = {Artificial intelligence, Bibliometric analysis, Cancer, Machine learning, Public health},\n\tpages = {96125--96137},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/HDQEC2S6/Ruiz-Fresneda et al. - 2023 - Bibliometric analysis of the global scientific production on machine learning applied to different c.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n Graph Neural Network Potentials for Molecular Dynamics Simulations of Water Cluster Anions.\n \n \n \n\n\n \n Gijón, A.; Molina-Solana, M.; and Gómez-Romero, J.\n\n\n \n\n\n\n In Mikyška, J.; de Mulatier, C.; Paszynski, M.; Krzhizhanovskaya, V. V.; Dongarra, J. J.; and Sloot, P. M., editor(s), Computational Science – ICCS 2023, pages 336–343, Cham, 2023. Springer Nature Switzerland\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 \n \n \n \n \n \n\n\n\n

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@inproceedings{gijon_graph_2023,\n\taddress = {Cham},\n\ttitle = {Graph {Neural} {Network} {Potentials} for {Molecular} {Dynamics} {Simulations} of {Water} {Cluster} {Anions}},\n\tcopyright = {All rights reserved},\n\tisbn = {978-3-031-36027-5},\n\tdoi = {10.1007/978-3-031-36027-5_25},\n\tabstract = {Regression of potential energy functions is one of the most popular applications of machine learning within the field of materials simulation since it would allow accelerating molecular dynamics simulations. Recently, graph-based architectures have been proven to be especially suitable for molecular systems. However, the construction of robust and transferable potentials, resulting in stable dynamical trajectories, still needs to be researched. In this work, we design and compare several neural architectures with different graph convolutional layers to predict the energy of water cluster anions, a system of fundamental interest in chemistry and biology. After identifying the best aggregation procedures for this problem, we have obtained accurate, fast-evaluated and easy-to-implement graph neural network models which could be employed in dynamical simulations in the future.},\n\tlanguage = {en},\n\tbooktitle = {Computational {Science} – {ICCS} 2023},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {Gijón, Alfonso and Molina-Solana, Miguel and Gómez-Romero, Juan},\n\teditor = {Mikyška, Jiří and de Mulatier, Clélia and Paszynski, Maciej and Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and Sloot, Peter M.A.},\n\tyear = {2023},\n\tkeywords = {Atomic Potentials, Convolutional layers, Graph Neural Networks, Message Passing, Molecular Dynamics},\n\tpages = {336--343},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/FU7K9B4C/Gijón et al. - 2023 - Graph Neural Network Potentials for Molecular Dynamics Simulations of Water Cluster Anions.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Physics-informed neural networks for data-driven simulation: Advantages, limitations, and opportunities.\n \n \n \n \n\n\n \n Fernández de la Mata, F.; Gijón, A.; Molina-Solana, M.; and Gómez-Romero, J.\n\n\n \n\n\n\n Physica A: Statistical Mechanics and its Applications, 610: 128415. January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Physics-informed$ 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 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

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@article{fernandez_de_la_mata_physics-informed_2023,\n\ttitle = {Physics-informed neural networks for data-driven simulation: {Advantages}, limitations, and opportunities},\n\tvolume = {610},\n\tcopyright = {All rights reserved},\n\tissn = {0378-4371},\n\tshorttitle = {Physics-informed neural networks for data-driven simulation},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0378437122009736},\n\tdoi = {10.1016/j.physa.2022.128415},\n\tabstract = {The last decade has seen a rise in the number and variety of techniques available for data-driven simulation of physical phenomena. One of the most promising approaches is Physics-Informed Neural Networks (PINNs), which can combine both data, obtained from sensors or numerical solvers, and physics knowledge, expressed as partial differential equations. In this work, we investigated the suitability of PINNs to replace current available numerical methods for physics simulations. Although the PINN approach is general and independent of the complexity of the underlying physics equations, a selection of typical heat transfer and fluid dynamics problems was proposed and multiple PINNs were comprehensibly trained and tested to solve them. When PINNs were used as learned simulators, the outcome of our experiments was not entirely satisfactory as not enough accuracy was achieved even though optimal configurations and long training times were used. The main cause for this limitation was found to be the lack of adequate activation functions and specialized architectures, since they proved to have a notable impact on the final accuracy of each model. In turn, PINN architectures showed an accurate behavior when used for parameter inference of partial differential equations from data.},\n\turldate = {2026-04-24},\n\tjournal = {Physica A: Statistical Mechanics and its Applications},\n\tauthor = {Fernández de la Mata, Félix and Gijón, Alfonso and Molina-Solana, Miguel and Gómez-Romero, Juan},\n\tmonth = jan,\n\tyear = {2023},\n\tkeywords = {Data-driven simulations, Deep learning, Learned simulators, Physics-Informed Neural Networks},\n\tpages = {128415},\n\tfile = {ScienceDirect Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/3X6LQ95L/Fernández de la Mata et al. - 2023 - Physics-informed neural networks for data-driven simulation Advantages, limitations, and opportunit.pdf:application/pdf;ScienceDirect Snapshot:/home/alfonso/snap/zotero-snap/common/Zotero/storage/LJTMEGCG/S0378437122009736.html:text/html},\n}\n\n

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\n \n 2022\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Quantum simulations of neutral water clusters and singly-charged water cluster anions.\n \n \n \n \n\n\n \n Gijón, A.; and R. Hernández, E.\n\n\n \n\n\n\n Physical Chemistry Chemical Physics, 24(23): 14440–14451. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantum$ 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 \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gijon_quantum_2022,\n\ttitle = {Quantum simulations of neutral water clusters and singly-charged water cluster anions},\n\tvolume = {24},\n\tcopyright = {All rights reserved},\n\turl = {https://pubs.rsc.org/en/content/articlelanding/2022/cp/d2cp01088g},\n\tdoi = {10.1039/D2CP01088G},\n\tlanguage = {en},\n\tnumber = {23},\n\turldate = {2026-04-24},\n\tjournal = {Physical Chemistry Chemical Physics},\n\tpublisher = {Royal Society of Chemistry},\n\tauthor = {Gijón, A. and R. Hernández, E.},\n\tyear = {2022},\n\tpages = {14440--14451},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/DGYN82JH/Gijón and R. Hernández - 2022 - Quantum simulations of neutral water clusters and singly-charged water cluster anions.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Quantum molecular simulations of micro-hydrated halogen anions - Physical Chemistry Chemical Physics (RSC Publishing) DOI:10.1039/D2CP01396G.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantum$ Paper\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

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@misc{noauthor_quantum_2022,\n\ttitle = {Quantum molecular simulations of micro-hydrated halogen anions - {Physical} {Chemistry} {Chemical} {Physics} ({RSC} {Publishing}) {DOI}:10.1039/{D2CP01396G}},\n\tcopyright = {All rights reserved},\n\turl = {https://pubs.rsc.org/en/content/articlehtml/2022/cp/d2cp01396g},\n\turldate = {2026-04-24},\n\tyear = {2022},\n}\n\n

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\n \n 2021\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Classical and quantum molecular dynamics simulations of condensed aqueous systems.\n \n \n \n \n\n\n \n Gijón Gijón, A.\n\n\n \n\n\n\n Ph.D. Thesis, Universidad Autónoma de Madrid, 2021.\n Pages: 1\n\n\n\n
\n\n\n\n \n \n $\"Classical$ Paper\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

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@phdthesis{gijon_gijon_classical_2021,\n\ttype = {http://purl.org/dc/dcmitype/{Text}},\n\ttitle = {Classical and quantum molecular dynamics simulations of condensed aqueous systems},\n\tcopyright = {All rights reserved},\n\turl = {https://dialnet.unirioja.es/servlet/tesis?codigo=303248},\n\tabstract = {In this thesis we have used molecular dynamics techniques to investigate the behavior and properties of water in condensed phases at the microscopic level. We have considered three different aqueous systems, studying the equilibrium properties for two of them and the relaxation from non-equilibrium states towards the equilibrium for the third one. On the one hand, we have address the solvation of negatively charged species in water clusters of various sizes. We have employed our own code (capable of performing classi- cal and path integral molecular dynamics simulations at finite temperature) to compute equilibrium properties of water clusters anions and halide ion water clusters. We have explicitly included nuclear quantum effects on the water dynamics, showing that such effects are non-negligible from low temperatures to room conditions. We have focused on the calculation of structural and energetic properties and observed the appearance of distinct solvation motifs of the different charged species in the water clusters. On the other hand, motivated by the controversial macroscopic Mpemba effect, we have explored the non-equilibrium properties of bulk water, which has contributed to at- tain a better understanding at the molecular level of how condensed systems approach thermodynamic equilibrium. We have analyzed the evolution of the kinetic energy com- ponents from initial states breaking the equilibrium partition of the kinetic energy among different modes. Doing so, we have observed the existence of a Mpemba-like microscopic effect in both liquid and solid water, and qualitatively related such effect with the equi- librium autocorrelation functions of the kinetic energy components.},\n\tlanguage = {es},\n\turldate = {2026-04-24},\n\tschool = {Universidad Autónoma de Madrid},\n\tauthor = {Gijón Gijón, Alfonso},\n\tyear = {2021},\n\tnote = {Pages: 1},\n\tfile = {Snapshot:/home/alfonso/snap/zotero-snap/common/Zotero/storage/3ESCV23L/tesis.html:text/html},\n}\n\n

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\n \n 2019\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Translucency of Graphene to van der Waals Forces Applies to Atoms/Molecules with Different Polar Character.\n \n \n \n \n\n\n \n Presel, F.; Gijón, A.; Hernández, E. R.; Lacovig, P.; Lizzit, S.; Alfè, D.; and Baraldi, A.\n\n\n \n\n\n\n ACS Nano, 13(10): 12230–12241. October 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Translucency$ 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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{presel_translucency_2019,\n\ttitle = {Translucency of {Graphene} to van der {Waals} {Forces} {Applies} to {Atoms}/{Molecules} with {Different} {Polar} {Character}},\n\tvolume = {13},\n\tcopyright = {All rights reserved},\n\tissn = {1936-0851},\n\turl = {https://doi.org/10.1021/acsnano.9b07277},\n\tdoi = {10.1021/acsnano.9b07277},\n\tabstract = {Graphene has been proposed to be either fully transparent to van der Waals interactions to the extent of allowing switching between hydrophobic and hydrophilic behavior, or partially transparent (translucent), yet there has been considerable debate on this topic, which is still ongoing. In a combined experimental and theoretical study we investigate the effects of different metal substrates on the adsorption energy of atomic (argon) and molecular (carbon monoxide) adsorbates on high-quality epitaxial graphene. We demonstrate that while the adsorption energy is certainly affected by the chemical composition of the supporting substrate and by the corrugation of the carbon lattice, the van der Waals interactions between adsorbates and the metal surfaces are partially screened by graphene. Our results indicate that the concept of graphene translucency, already introduced in the case of water droplets, is found to hold more generally also in the case of single polar molecules and atoms, which are apolar.},\n\tnumber = {10},\n\turldate = {2026-04-24},\n\tjournal = {ACS Nano},\n\tpublisher = {American Chemical Society},\n\tauthor = {Presel, Francesco and Gijón, Alfonso and Hernández, Eduardo R. and Lacovig, Paolo and Lizzit, Silvano and Alfè, Dario and Baraldi, Alessandro},\n\tmonth = oct,\n\tyear = {2019},\n\tpages = {12230--12241},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/MUTG75WB/Presel et al. - 2019 - Translucency of Graphene to van der Waals Forces Applies to AtomsMolecules with Different Polar Cha.pdf:application/pdf},\n}\n\n

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\n \n\n \n \n \n \n \n \n Paths towards equilibrium in molecular systems: The case of water.\n \n \n \n \n\n\n \n Gijón, A.; Lasanta, A.; and Hernández, E. R.\n\n\n \n\n\n\n Physical Review E, 100(3): 032103. September 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Paths$ 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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gijon_paths_2019,\n\ttitle = {Paths towards equilibrium in molecular systems: {The} case of water},\n\tvolume = {100},\n\tcopyright = {All rights reserved},\n\tshorttitle = {Paths towards equilibrium in molecular systems},\n\turl = {https://link.aps.org/doi/10.1103/PhysRevE.100.032103},\n\tdoi = {10.1103/PhysRevE.100.032103},\n\tabstract = {We consider the problem of how a condensed molecular system approaches equilibrium, focusing on the particular case of water. We show, by means of extensive molecular dynamics simulations, that the existence of different types of degrees of freedom affects the dynamics of equilibration, and this influence is made most obvious in the system's temperature. When equipartition of energy does not hold in the initial, nonequilibrium state, the instantaneous temperature can be up to a few degrees lower than that observed under equipartition conditions, resulting in a Mpemba-like effect. Though our study considers water in particular, our findings apply more generally to condensed molecular systems.},\n\tnumber = {3},\n\turldate = {2026-04-24},\n\tjournal = {Physical Review E},\n\tpublisher = {American Physical Society},\n\tauthor = {Gijón, A. and Lasanta, A. and Hernández, E. R.},\n\tmonth = sep,\n\tyear = {2019},\n\tpages = {032103},\n\tfile = {Submitted Version:/home/alfonso/snap/zotero-snap/common/Zotero/storage/7B9C4CVP/Gijón et al. - 2019 - Paths towards equilibrium in molecular systems The case of water.pdf:application/pdf},\n}\n\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 \n \n \n Wave Packet Calculations of the Quantum Transport of Atoms through Nanoporous Membranes.\n \n \n \n \n\n\n \n Gijón, A.; Campos-Martínez, J.; and Hernández, M. I.\n\n\n \n\n\n\n The Journal of Physical Chemistry C, 121(36): 19751–19757. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Wave$ 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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gijon_wave_2017,\n\ttitle = {Wave {Packet} {Calculations} of the {Quantum} {Transport} of {Atoms} through {Nanoporous} {Membranes}},\n\tvolume = {121},\n\tcopyright = {All rights reserved},\n\tissn = {1932-7447},\n\turl = {https://doi.org/10.1021/acs.jpcc.7b04298},\n\tdoi = {10.1021/acs.jpcc.7b04298},\n\tabstract = {Quantum phenomena are relevant to the transport of light atoms and molecules through nanoporous membranes. Indeed, confinement provided by (sub)nanometer pores enhances quantum effects such as tunneling and zero point energy (ZPE), even leading to quantum sieving of different isotopes of a given element. In this work, we apply rigorous wave packet techniques to the simulation in the three-dimensional space of the passage of atoms through periodic rigid one-atom-thick membranes. We aim to assess more approximate quantum models such as one-dimensional tunneling calculations or transition state theory (TST). Calculations are reported for the transmission of 3He and 4He through graphdiyne as well as through a holey graphene model. For He–graphdiyne, conclusions from tunneling-corrected TST are confirmed: both tunneling and ZPE effects are significant, but competition between each other leads to a moderately small 4He/3He selectivity, in stark contrast with predictions from one-dimensional tunneling calculations. For the transport of He isotopes through leaky graphene, the computed transmission probabilities are highly structured suggesting widespread selective adsorption resonances and, this time, rate coefficients and selectivity ratios do not agree well with estimations from TST. This study intends to go one step further toward a better understanding of the quantum-mechanical processes in the transport of gases through nanoporous membranes.},\n\tnumber = {36},\n\turldate = {2026-04-24},\n\tjournal = {The Journal of Physical Chemistry C},\n\tpublisher = {American Chemical Society},\n\tauthor = {Gijón, Alfonso and Campos-Martínez, José and Hernández, Marta I.},\n\tmonth = sep,\n\tyear = {2017},\n\tpages = {19751--19757},\n\tfile = {Full Text PDF:/home/alfonso/snap/zotero-snap/common/Zotero/storage/4QM6558B/Gijón et al. - 2017 - Wave Packet Calculations of the Quantum Transport of Atoms through Nanoporous Membranes.pdf:application/pdf},\n}\n\n

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\n \n 2016\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Anisotropic multicluster model in light nuclei.\n \n \n \n \n\n\n \n Gijón, A; Gálvez, F J; Saavedra, F A. d.; and Buendía, E\n\n\n \n\n\n\n Journal of Physics G: Nuclear and Particle Physics, 43(6): 065103. April 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Anisotropic$ 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 abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gijon_anisotropic_2016,\n\ttitle = {Anisotropic multicluster model in light nuclei},\n\tvolume = {43},\n\tcopyright = {All rights reserved},\n\tissn = {0954-3899},\n\turl = {https://doi.org/10.1088/0954-3899/43/6/065103},\n\tdoi = {10.1088/0954-3899/43/6/065103},\n\tabstract = {Multicluster models consider that the nucleons can be moving around different centers in the nuclei. These models have been widely used to describe light nuclei but always considering that the mean field is composed of isotropic harmonic oscillators with different centers. In this work, we propose an extension of these models by using anisotropic harmonic oscillators. The strengths of these oscillators, the distance among the different centers and the disposition of the nucleons inside every cluster are free parameters which have been fixed using the variational criterion. All the one-body and two-body matrix elements have been analytically calculated. Only a numerical integration on the Euler angles is needed to carry out the projection on the values of the total spin of the state and its third component. We have studied the ground state and the first excited states of 8Be, 12C and 10Be getting good results for the energies. The disposition of the nucleons in the different clusters have also been analyzed by using projection on the different Cartesian planes getting much more information than when the radial one-body density is used.},\n\tlanguage = {en},\n\tnumber = {6},\n\turldate = {2026-04-24},\n\tjournal = {Journal of Physics G: Nuclear and Particle Physics},\n\tpublisher = {IOP Publishing},\n\tauthor = {Gijón, A and Gálvez, F J and Saavedra, F Arias de and Buendía, E},\n\tmonth = apr,\n\tyear = {2016},\n\tpages = {065103},\n\tfile = {Submitted Version:/home/alfonso/snap/zotero-snap/common/Zotero/storage/BM2AKWHZ/Gijón et al. - 2016 - Anisotropic multicluster model in light nuclei.pdf:application/pdf},\n}\n

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