Machine-Learned Fragment-Based Energies for Crystal Structure Prediction

Journal of Chemical Theory and Computation
David McDonaghGraeme M Day

Abstract

Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevent a reliable assessment of the relative thermodynamic stability of potential structures, while the cost of fully quantum mechanical approaches can limit applications of the methods. We present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach and predicting these corrections with machine learning. Corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve with the fragment corrections. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results using as little ...Continue Reading

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Citations

Oct 27, 2020·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Scott M WoodleyR Catlow
Jan 12, 2021·Journal of Chemical Theory and Computation·Pablo A UnzuetaGregory J O Beran
Feb 11, 2021·Advanced Materials·Rebecca L Greenaway, Kim E Jelfs
Dec 10, 2020·The Journal of Chemical Physics·Janus J Eriksen
Mar 30, 2021·Chemical Reviews·Jörg Behler
Jun 25, 2021·Chemical Science·Simon WengertJohannes T Margraf
Oct 21, 2020·The Journal of Physical Chemistry. a·B Christopher Rinderspacher
Sep 4, 2020·The Journal of Physical Chemistry. a·Olga EgorovaGraeme M Day
Dec 18, 2021·Journal of the American Chemical Society·John E Carpenter, Michael Grünwald

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