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, 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1578-1597 |
| Number of pages | 20 |
| Journal | Matter |
| Volume | 4 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 5 2021 |
| Externally published | Yes |
Funding
A.S.R. was supported by a fellowship award through the National Defense Science and Engineering Graduate Fellowship Program, sponsored by the Air Force Research Laboratory (AFRL), the Office of Naval Research , and the Army Research Office . A.S.R. also acknowledges support by a Ryan Fellowship from the International Institute for Nanotechnology and a Presidential Fellowship from The Graduate School at Northwestern University . This work was further supported by the US Department of Energy , Office of Basic Energy Sciences , Division of Chemical Sciences, Geosciences and Biosciences through the Nanoporous Materials Genome Center under award number DE-FG02-17ER16362. The authors acknowledge computing support from the Extreme Science and Engineering Discovery Environment (XSEDE) Stampede2 through allocation CTS180057 supported by National Science Foundation grant number ACI-1548562 (A.S.R., R.Q.S.), the Quest High Performance Computing (HPC) facility at Northwestern University (A.S.R., S.M.I., R.Q.S.), the Mustang HPC environment via the Department of Defense High Performance Computing Modernization Program at the AFRL (A.S.R.), and the Minnesota Supercomputing Institute at the University of Minnesota (D.R., L.G.). A.A.-G. thanks Dr. Anders G. Frøseth for his generous support.
Keywords
- MAP3: Understanding
- artificial intelligence
- band gap
- database
- density functional theory
- electronic structure
- high-throughput screening
- machine learning
- materials discovery
- metal–organic framework
- quantum chemistry