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

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Abstract
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.
Keywords
Computer Science Applications, Mechanics of Materials, General Materials Science, Modeling and Simulation

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MLA
Vandenhaute, Sander, et al. “Machine Learning Potentials for Metal-Organic Frameworks Using an Incremental Learning Approach.” NPJ COMPUTATIONAL MATERIALS, vol. 9, no. 1, 2023, doi:10.1038/s41524-023-00969-x.
APA
Vandenhaute, S., Cools-Ceuppens, M., DeKeyser, S., Verstraelen, T., & Van Speybroeck, V. (2023). Machine learning potentials for metal-organic frameworks using an incremental learning approach. NPJ COMPUTATIONAL MATERIALS, 9(1). https://doi.org/10.1038/s41524-023-00969-x
Chicago author-date
Vandenhaute, Sander, Maarten Cools-Ceuppens, Simon DeKeyser, Toon Verstraelen, and Veronique Van Speybroeck. 2023. “Machine Learning Potentials for Metal-Organic Frameworks Using an Incremental Learning Approach.” NPJ COMPUTATIONAL MATERIALS 9 (1). https://doi.org/10.1038/s41524-023-00969-x.
Chicago author-date (all authors)
Vandenhaute, Sander, Maarten Cools-Ceuppens, Simon DeKeyser, Toon Verstraelen, and Veronique Van Speybroeck. 2023. “Machine Learning Potentials for Metal-Organic Frameworks Using an Incremental Learning Approach.” NPJ COMPUTATIONAL MATERIALS 9 (1). doi:10.1038/s41524-023-00969-x.
Vancouver
1.
Vandenhaute S, Cools-Ceuppens M, DeKeyser S, Verstraelen T, Van Speybroeck V. Machine learning potentials for metal-organic frameworks using an incremental learning approach. NPJ COMPUTATIONAL MATERIALS. 2023;9(1).
IEEE
[1]
S. Vandenhaute, M. Cools-Ceuppens, S. DeKeyser, T. Verstraelen, and V. Van Speybroeck, “Machine learning potentials for metal-organic frameworks using an incremental learning approach,” NPJ COMPUTATIONAL MATERIALS, vol. 9, no. 1, 2023.
@article{01GSYNMHEZB1TMQNBSTPR23YGN,
  abstract     = {{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.}},
  articleno    = {{19}},
  author       = {{Vandenhaute, Sander and Cools-Ceuppens, Maarten and DeKeyser, Simon and Verstraelen, Toon and Van Speybroeck, Veronique}},
  issn         = {{2057-3960}},
  journal      = {{NPJ COMPUTATIONAL MATERIALS}},
  keywords     = {{Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{8}},
  title        = {{Machine learning potentials for metal-organic frameworks using an incremental learning approach}},
  url          = {{http://doi.org/10.1038/s41524-023-00969-x}},
  volume       = {{9}},
  year         = {{2023}},
}

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