Expanding materials science with universal many-body graph neural networks

被引：2
|
作者：
Pan, Jie
机构：
来源：
NATURE COMPUTATIONAL SCIENCE | 2022年 / 2卷 / 11期
关键词：
D O I：
10.1038/s43588-022-00360-8
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A universal interatomic potential for the periodic table has been developed by combining graph neural networks with three-body interactions. This M3GNet potential can perform structural relaxations, dynamic simulations and property predictions for materials across a diverse chemical space.
引用
收藏
页码：703 / 704
页数：2
相关论文
共 50 条
  • [21] Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions
    Luo, Di
    Clark, Bryan K.
    PHYSICAL REVIEW LETTERS, 2019, 122 (22)
  • [22] Approximating quantum many-body wave functions using artificial neural networks
    Cai, Zi
    Liu, Jinguo
    PHYSICAL REVIEW B, 2018, 97 (03)
  • [23] Constructing exact representations of quantum many-body systems with deep neural networks
    Giuseppe Carleo
    Yusuke Nomura
    Masatoshi Imada
    Nature Communications, 9
  • [24] Quantum neural networks to detect entanglement transitions in quantum many-body systems
    El Ayachi, F.
    Mansour, H. Ait
    El Baz, M.
    PHYSICA SCRIPTA, 2025, 100 (01)
  • [25] Constructing exact representations of quantum many-body systems with deep neural networks
    Carleo, Giuseppe
    Nomura, Yusuke
    Imada, Masatoshi
    NATURE COMMUNICATIONS, 2018, 9
  • [26] Many-body interactions in soft jammed materials
    Hoehler, Reinhard
    Cohen-Addad, Sylvie
    SOFT MATTER, 2017, 13 (07) : 1371 - 1383
  • [27] Coupled Cluster Downfolding Theory: towards universal many-body algorithms for dimensionality reduction of composite quantum systems in chemistry and materials science
    Nicholas P. Bauman
    Karol Kowalski
    Materials Theory, 6 (1):
  • [28] Spectral tensor networks for many-body localization
    Chandran, A.
    Carrasquilla, J.
    Kim, I. H.
    Abanin, D. A.
    Vidal, G.
    PHYSICAL REVIEW B, 2015, 92 (02)
  • [29] Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks
    Rrapaj, Ermal
    Roggero, Alessandro
    PHYSICAL REVIEW E, 2021, 103 (01)
  • [30] Extracting electronic many-body correlations from local measurements with artificial neural networks
    Aikebaier, Faluke
    Ojanen, Teemu
    Lado, Jose L.
    SCIPOST PHYSICS CORE, 2023, 6 (02): : 1 - 15
12345