Machine-learned potentials for eucryptite: A systematic comparison

被引：0
|
作者：
Jörg-Rüdiger Hill
Wolfgang Mannstadt
机构：
[1] Materials Design SARL,
[2] SCHOTT AG,undefined
来源：
Journal of Materials Research | 2023年 / 38卷
关键词：
Machine-learned potential; Eucryptite; Thermal expansion; Ionic conductivity; Molecular dynamics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：5188 / 5197
页数：9
相关论文
共 50 条
  • [1] Machine-learned potentials for eucryptite: A systematic comparison
    Hill, Jorg-Rudiger
    Mannstadt, Wolfgang
    [J]. JOURNAL OF MATERIALS RESEARCH, 2023, 38 (24) : 5188 - 5197
  • [2] How to validate machine-learned interatomic potentials
    Morrow, Joe D.
    Gardner, John L. A.
    Deringer, Volker L.
    [J]. JOURNAL OF CHEMICAL PHYSICS, 2023, 158 (12):
  • [3] A FRAMEWORK FOR A GENERALIZATION ANALYSIS OF MACHINE-LEARNED INTERATOMIC POTENTIALS
    Ortner, Christoph
    Wang, Yangshuai
    [J]. MULTISCALE MODELING & SIMULATION, 2023, 21 (03): : 1053 - 1080
  • [4] A theoretical case study of the generalization of machine-learned potentials
    Wang, Yangshuai
    Patel, Shashwat
    Ortner, Christoph
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2024, 422
  • [5] Simple machine-learned interatomic potentials for complex alloys
    Byggmastar, J.
    Nordlund, K.
    Djurabekova, F.
    [J]. PHYSICAL REVIEW MATERIALS, 2022, 6 (08):
  • [6] Machine-learned interatomic potentials for alloys and alloy phase diagrams
    Rosenbrock, Conrad W.
    Gubaev, Konstantin
    Shapeev, Alexander V.
    Partay, Livia B.
    Bernstein, Noam
    Csanyi, Gabor
    Hart, Gus L. W.
    [J]. NPJ COMPUTATIONAL MATERIALS, 2021, 7 (01)
  • [7] Machine-learned interatomic potentials: Recent developments and prospective applications
    Eyert, Volker
    Wormald, Jonathan
    Curtin, William A.
    Wimmer, Erich
    [J]. JOURNAL OF MATERIALS RESEARCH, 2023, 38 (24) : 5079 - 5094
  • [8] Machine-learned potentials for next-generation matter simulations
    Pascal Friederich
    Florian Häse
    Jonny Proppe
    Alán Aspuru-Guzik
    [J]. Nature Materials, 2021, 20 : 750 - 761
  • [9] Machine-learned interatomic potentials: Recent developments and prospective applications
    Volker Eyert
    Jonathan Wormald
    William A. Curtin
    Erich Wimmer
    [J]. Journal of Materials Research, 2023, 38 : 5079 - 5094
  • [10] Machine-learned interatomic potentials for alloys and alloy phase diagrams
    Conrad W. Rosenbrock
    Konstantin Gubaev
    Alexander V. Shapeev
    Livia B. Pártay
    Noam Bernstein
    Gábor Csányi
    Gus L. W. Hart
    [J]. npj Computational Materials, 7
12345