Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Metal–organic frameworks (MOFs) are a widely investigated class of crystalline

solids with tunable structures that make it possible to impart specific

chemical functionality tailored for a given application. However, the enormous

number of possible MOFs that can be synthesized makes it difficult to determine

which materials would be the most promising candidates, especially for

applications governed by electronic structure properties that are often

computationally demanding to simulate and time-consuming to probe

experimentally. Here, we have developed the first publicly available quantum-chemical

database for MOFs (the “QMOF database”), which consists of properties derived

from density functional theory (DFT) for over 14,000 experimentally synthesized

MOFs. Throughout this study, we demonstrate how this new database can be used

to identify MOFs with targeted electronic structure properties. As a

proof-of-concept, we use the QMOF database to evaluate the performance of

several machine learning models for the prediction of DFT-computed band gaps

and find that crystal graph convolutional neural networks are capable of

achieving superior predictive performance, making it possible to circumvent

computationally expensive quantum-chemical calculations. We also show how

unsupervised learning methods can aid the discovery of otherwise subtle structure–property

relationships using the computational findings in this work. We conclude by

highlighting several MOFs with low band gaps, a challenging task given the

electronically insulating nature of most MOF structures. The data and

predictive models generated in this work, as well as the database of MOF

structures, should be highly useful to other researchers interested in the

predictive design and discovery of MOFs for the many applications dictated by

quantum-chemical phenomena.