Chemically intuited, large-scale screening of MOFs by machine learning techniques. Borboudakis, G., Stergiannakos, T., Frysali, M., Klontzas, E., Tsamardinos, I., & Froudakis, G. E. npj Computational Materials, 3(1):40, October, 2017. ![Chemically intuited, large-scale screening of MOFs by machine learning techniques [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.
@article{borboudakis_chemically_2017,
title = {Chemically intuited, large-scale screening of {MOFs} by machine learning techniques},
volume = {3},
issn = {2057-3960},
url = {https://doi.org/10.1038/s41524-017-0045-8},
doi = {10.1038/s41524-017-0045-8},
abstract = {A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.},
number = {1},
journal = {npj Computational Materials},
author = {Borboudakis, Giorgos and Stergiannakos, Taxiarchis and Frysali, Maria and Klontzas, Emmanuel and Tsamardinos, Ioannis and Froudakis, George E.},
month = oct,
year = {2017},
pages = {40},
}
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