Data-Driven Prediction of Structures of Metal–Organic Frameworks
Crystal structure prediction (CSP) has proven to be an effective route for the discovery of new materials. Nonetheless, the <i>ab initio</i> techniques employed for the CSP of metal-organic frameworks (MOFs) cannot be scaled to a high-throughput mode. Here, we propose a data-driven method for addressing the current needs of computational MOF discovery. Specifically, coarse-grained neural networks were implemented to predict the underlying net topology. The models showed satisfactory performance, which was next enhanced via the limitation of the applicability domain.
Authors
- Artem A. Mitrofanov (ORCID: https://orcid.org/0000-0001-8891-6862)
- Anatoliy A. Chasovskikh
- Iurii M. Nevolin (ORCID: https://orcid.org/0000-0001-7008-4586)
- Vadim Korolev (ORCID: https://orcid.org/0000-0001-6117-5662)
- Elizaveta I. Yakovenko
Institutions
- Russian Academy of Sciences (RU)
- Lomonosov Moscow State University (RU)
- Frumkin Institute of Physical Chemistry and Electrochemistry Russian Academy of Sciences (RU)
- Moscow State University (TJ)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2025-02-10
- DOI
- https://doi.org/10.1021/acs.jcim.4c02367
- Citations
- 2
- Primary Topic
- Metal-Organic Frameworks: Synthesis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.07