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

By Andrew S. Rosen, Shaelyn Iyer, Debmalya Ray, Zhenpeng Yao, Alán Aspuru-Guzik, L. Gagliardi, Justin M. Notestein, Randall Q. Snurr. Matter, 2020

454 citations recorded by the source corpus.

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.

Subfields: Inorganic Chemistry, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/515d5d3010e391fc8bb0026269e92db5e60a907a

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In Inorganic Chemistry

In Materials Chemistry

Citations in the corpus

A foundation model for atomistic materials chemistry. (2023, unknown): “We tested our pre-trained model directly against version 14 of the Quantum MOF (QMOF) database, which contains DFT-computed properties at several levels of theory for 20,000+ MOFs and structurally related coordination polymers ( 81,82 ).”
Recent advances and applications of deep learning methods in materials science (2021, methodology): “Rosen et al. 124 used graph neural network models to predict the bandgaps of metal-organic frameworks.”
LitMOF: An LLM Multi-Agent for Literature-Validated Metal-Organic Frameworks Database Correction and Expansion (2025, unknown): “This diversity has motivated creation of large curated databases, including the CoRE MOF databases[2–4], the CSD MOF subset[5, 6], QMOF database[7, 8], and MOSAEC-DB[9], which serve as the foundation for high-throughput simulations[10–12], machine learning based property prediction[13–17], and…”