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

Krynski, M., & Rossi, M. (2021). Efficient Gaussian process regression for prediction of molecular crystals harmonic free energies. npj Computational Materials, 7(1): 169. doi:10.1038/s41524-021-00638-x.

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Open Access. - This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. - Open Access funding enabled and organized by Projekt DEAL.
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https://arxiv.org/abs/2106.08612 (Preprint)
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 Creators:
Krynski, M.1, 2, Author
Rossi, M.1, 3, Author           
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1Fritz Haber Institute of the Max Planck Society, ou_persistent22              
2Faculty of Physics, Warsaw University of Technology, ou_persistent22              
3Simulations from Ab Initio Approaches, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3185035              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2021-01-112021-09-212021-10-15
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: arXiv: 2106.08612
DOI: 10.1038/s41524-021-00638-x
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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 7 (1) Sequence Number: 169 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960