Efficient Gaussian process regression for prediction of molecular crystals harmonic free energies

We present a method to accurately predict the Helmholtz harmonic free energies of molecular crystals in high-throughput settings. This is achieved by devising a computationally efficient framework that employs a Gaussian Process Regression model based on local atomic environments. The cost to train...

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Bibliographic Details
Published in:npj computational materials 2021-10, Vol.7 (1), p.1-10, Article 169
Main Authors: Krynski, Marcin, Rossi, Mariana
Format: Article
Language:English
Subjects:
639/301/1034/1037
639/766/119/2795
Accuracy
Aromatic hydrocarbons
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Crystal lattices
Crystal structure
Crystals
Density functional theory
Gaussian process
Hydrocarbons
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Optimization
Regression models
Theoretical
Training
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Summary:We present a method to accurately predict the Helmholtz harmonic free energies of molecular crystals in high-throughput settings. This is achieved by devising a computationally efficient framework that employs a Gaussian Process Regression model based on local atomic environments. The cost to train the model with ab initio potentials is reduced by starting the optimization of the framework parameters, as well as the training and validation sets, with an empirical potential. This is then transferred to train the model based on density-functional theory potentials, including dispersion-corrections. We benchmarked our framework on a set of 444 hydrocarbon crystal structures, comprising 38 polymorphs and 406 crystal structures either measured in different conditions or derived from these polymorphs. Superior performance and high prediction accuracy, with mean absolute deviation below 0.04 kJ mol −1 per atom at 300 K is achieved by training on as little as 60 crystal structures. Furthermore, we demonstrate the predictive efficiency and accuracy of the developed framework by successfully calculating the thermal lattice expansion of aromatic hydrocarbon crystals within the quasi-harmonic approximation, and predict how lattice expansion affects the polymorph stability ranking.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-021-00638-x