Machine learning potentials for metal-organic frameworks using an incremental learning approach

Computational modeling of physical processes in metal-organic frameworks (MOFs) is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior. Density functional theory (DFT) may describe interatomic interactions at the quantum mech...

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Bibliographic Details
Published in:npj computational materials 2023-02, Vol.9 (1), p.19-8, Article 19
Main Authors: Vandenhaute, Sander, Cools-Ceuppens, Maarten, DeKeyser, Simon, Verstraelen, Toon, Van Speybroeck, Veronique
Format: Article
Language:English
Subjects:
639/301/1034/1037
639/766/119/2795
639/925/357/404
Accuracy
Active learning
Algorithms
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Computer applications
Density functional theory
Energy
Equilibrium
Iterative methods
Learning algorithms
Machine learning
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Metal-organic frameworks
Neural networks
Quantum mechanics
Theoretical
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Summary:Computational modeling of physical processes in metal-organic frameworks (MOFs) is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior. Density functional theory (DFT) may describe interatomic interactions at the quantum mechanical level, but is computationally too expensive for systems beyond the nanometer and picosecond range. Herein, we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs. The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner. With only a few hundred single-point DFT evaluations per material, accurate and transferable potentials are obtained, even for flexible frameworks with multiple structurally different phases. The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-023-00969-x