Machine-learning correction to density-functional crystal structure optimization

被引:0
|
作者
Robert Hussein
Jonathan Schmidt
Tomás Barros
Miguel A. L. Marques
Silvana Botti
机构
[1] Friedrich-Schiller-Universität Jena,Institut für Festkörpertheorie und
[2] European Theoretical Spectroscopy Facility,optik
[3] Martin-Luther-Universität Halle-Wittenberg,Institut für Physik
来源
MRS Bulletin | 2022年 / 47卷
关键词
Machine learning; Crystallographic structure; Predictive;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:765 / 771
页数:6
相关论文
共 50 条
  • [1] Machine-learning correction to density-functional crystal structure optimization
    Hussein, Robert
    Schmidt, Jonathan
    Barros, Tomas
    Marques, Miguel A. L.
    Botti, Silvana
    MRS BULLETIN, 2022, 47 (08) : 765 - 771
  • [2] A semilocal machine-learning correction to density functional approximations
    Wang, JingChun
    Wang, Yao
    Xu, Rui-Xue
    Chen, GuanHua
    Zheng, Xiao
    JOURNAL OF CHEMICAL PHYSICS, 2023, 158 (15):
  • [3] Machine learning the derivative discontinuity of density-functional theory
    Gedeon, Johannes
    Schmidt, Jonathan
    Hodgson, Matthew J. P.
    Wetherell, Jack
    Benavides-Riveros, Carlos L.
    Marques, Miguel A. L.
    MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2022, 3 (01):
  • [4] Pressure correction in density-functional calculations
    Lee, Shun Hang
    Wan, Jones Tsz Kai
    PHYSICAL REVIEW B, 2008, 78 (22):
  • [5] Development of a machine-learning finite-range nonlocal density functional
    Chen, Zehua
    Yang, Weitao
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 258
  • [6] Machine Learning the Physical Nonlocal Exchange-Correlation Functional of Density-Functional Theory
    Schmidt, Jonathan
    Benavides-Riveros, Carlos L.
    Marques, Miguel A. L.
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2019, 10 (20): : 6425 - 6431
  • [7] Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
    Podryabinkin, Evgeny, V
    Tikhonov, Evgeny, V
    Shapeev, Alexander, V
    Oganov, Artem R.
    PHYSICAL REVIEW B, 2019, 99 (06)
  • [8] Machine-learning potentials for crystal defects
    Rodrigo Freitas
    Yifan Cao
    MRS Communications, 2022, 12 : 510 - 520
  • [9] Machine-learning potentials for crystal defects
    Freitas, Rodrigo
    Cao, Yifan
    MRS COMMUNICATIONS, 2022, 12 (05) : 510 - 520
  • [10] Which molecules can challenge density-functional tight-binding methods in evaluating the energies of conformers? investigation with machine-learning toolset
    Terets, Andrii
    Nikolaienko, Tymofii
    LOW TEMPERATURE PHYSICS, 2024, 50 (03) : 227 - 235