Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Evgeny V. Podryabinkin, Evgeny V. Tikhonov, Alexander V. Shapeev, Artem R. Oganov

Research output: Contribution to journalArticlepeer-review

142 Citations (Scopus)

Abstract

We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on prediction of crystal structures of carbon, high-pressure phases of sodium, and boron allotropes, including those that have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced, and a hitherto unknown 54-atom structure of boron has been predicted with very modest computational effort.

Original languageEnglish
Article number064114
JournalPhysical Review B
Volume99
Issue number6
DOIs
Publication statusPublished - 27 Feb 2019

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