2025-03-04 統計数理研究所
＜関連情報＞
- https://www.ism.ac.jp/ura/press/ISM2024-08.html
- https://www.ism.ac.jp/ura/press/ISM2024-08/pr250304.pdf
- https://www.nature.com/articles/s41524-024-01471-8
機械学習による形成エネルギーを用いたショットガン結晶構造予測 Shotgun crystal structure prediction using machine-learned formation energies
Liu Chang,Hiromasa Tamaki,Tomoyasu Yokoyama,Kensuke Wakasugi,Satoshi Yotsuhashi,Minoru Kusaba,Artem R. Oganov &Ryo Yoshida
npj Computational Materials Published:20 December 2024
DOI:https://doi.org/10.1038/s41524-024-01471-8
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.
