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Updated: May 28, 2025
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
Published on: October 6, 2023
Data-Driven Prediction of Structures of Metal-Organic Frameworks
Elizaveta I Yakovenko1, Iurii M Nevolin2, Anatoliy A Chasovskikh3
1MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia.
Abstract:
Crystal structure prediction (CSP) has proven to be an effective route for the discovery of new materials. Nonetheless, the ab initio 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.
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