Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew Rosen, Shaelyn Iyer, Debmalya Ray, Zhenpeng Yao, Alán Aspuru‐Guzik, Laura Gagliardi, Justin M. Notestein, Randall Q. Snurr
Abstract
The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here in this paper, we introduce the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties, using the prediction of theoretically computed band gaps as a representative example. We conclude by highlighting several MOFs predicted to have low band gaps, a challenging task given the electronically insulating nature of most MOFs.