Global machine learning potentials for molecular crystals

Molecular crystals are difficult to model with accurate first-principles methods due to large unit cells. On the other hand, accurate modeling is required as polymorphs often differ by only 1 kJ/mol. Machine learning interatomic potentials promise to provide accuracy of the baseline first-principles...

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Bibliographic Details
Published in:The Journal of chemical physics 2024-04, Vol.160 (15)
Main Authors: Žugec, Ivan, Geilhufe, R. Matthias, Lončarić, Ivor
Format: Article
Language:English
Subjects:
Crystals
Density functional theory
First principles
Machine learning
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Summary:Molecular crystals are difficult to model with accurate first-principles methods due to large unit cells. On the other hand, accurate modeling is required as polymorphs often differ by only 1 kJ/mol. Machine learning interatomic potentials promise to provide accuracy of the baseline first-principles methods with a cost lower by orders of magnitude. Using the existing databases of the density functional theory calculations for molecular crystals and molecules, we train global machine learning interatomic potentials, usable for any molecular crystal. We test the performance of the potentials on experimental benchmarks and show that they perform better than classical force fields and, in some cases, are comparable to the density functional theory calculations.
ISSN:0021-9606
1089-7690
1089-7690
DOI:10.1063/5.0196232