Machine-learning potentials for crystal defects

被引:0
|
作者
Rodrigo Freitas
Yifan Cao
机构
[1] Massachusetts Institute of Technology,Department of Materials Science and Engineering
来源
MRS Communications | 2022年 / 12卷
关键词
Computation/computing; Microstructure; Crystal; Defects; Machine learning; Modeling;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:510 / 520
页数:10
相关论文
共 50 条
  • [1] Machine-learning potentials for crystal defects
    Freitas, Rodrigo
    Cao, Yifan
    [J]. MRS COMMUNICATIONS, 2022, 12 (05) : 510 - 520
  • [2] Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
    Podryabinkin, Evgeny, V
    Tikhonov, Evgeny, V
    Shapeev, Alexander, V
    Oganov, Artem R.
    [J]. PHYSICAL REVIEW B, 2019, 99 (06)
  • [3] Training machine-learning potentials for crystal structure prediction using disordered structures
    Hong, Changho
    Choi, Jeong Min
    Jeong, Wonseok
    Kang, Sungwoo
    Ju, Suyeon
    Lee, Kyeongpung
    Jung, Jisu
    Youn, Yong
    Han, Seungwu
    [J]. PHYSICAL REVIEW B, 2020, 102 (22)
  • [5] Heat flux for semilocal machine-learning potentials
    Langer, Marcel F.
    Knoop, Florian
    Carbogno, Christian
    Scheffler, Matthias
    Rupp, Matthias
    [J]. Physical Review B, 2023, 108 (10)
  • [6] Ultra-fast interpretable machine-learning potentials
    Stephen R. Xie
    Matthias Rupp
    Richard G. Hennig
    [J]. npj Computational Materials, 9
  • [7] Ultra-fast interpretable machine-learning potentials
    Xie, Stephen R.
    Rupp, Matthias
    Hennig, Richard G.
    [J]. NPJ COMPUTATIONAL MATERIALS, 2023, 9 (01)
  • [8] Improve the performance of machine-learning potentials by optimizing descriptors
    Gao, Hao
    Wang, Junjie
    Sun, Jian
    [J]. JOURNAL OF CHEMICAL PHYSICS, 2019, 150 (24):
  • [9] Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W
    Goryaeva, Alexandra M.
    Deres, Julien
    Lapointe, Clovis
    Grigorev, Petr
    Swinburne, Thomas D.
    Kermode, James R.
    Ventelon, Lisa
    Baima, Jacopo
    Marinica, Mihai-Cosmin
    [J]. PHYSICAL REVIEW MATERIALS, 2021, 5 (10)
  • [10] Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
    Byggmastar, J.
    Nikoulis, G.
    Fellman, A.
    Granberg, F.
    Djurabekova, F.
    Nordlund, K.
    [J]. JOURNAL OF PHYSICS-CONDENSED MATTER, 2022, 34 (30)