Year: 2024
Type: article
Abstract: Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.
Source: npj Computational Materials
Authors: Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Artem R. Oganov, Ryo Yoshida
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,290
Awards: G1254741201, Transfer learning to predict the properties of compounds: pre-trained model library, G1556034498, G1799458091, G1809563441, G1838689680, Discovery of innovative functional materials using state-of-the-art machine learning, G2043847325, Construction of an unbiased and synthesizable inorganic database from data-driven, G2410738859, G3006590441, G3360682306, G3899256072, G6718509927, G7879866177
OpenAlex ID: W4405654565