Many-body representations for machine learning models of molecular properties

被引：0
|
作者：
Huang, Bing [1 ]
von Lilienfeld, O. Anatole [1 ]
机构：
[1] Univ Basel, Dept Chem, Basel, Switzerland
来源：
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY | 2016年 / 252卷
关键词：
D O I：
暂无
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
244
引用
下载
收藏
页数：1
相关论文
共 50 条
  • [1] Certificates of quantum many-body properties assisted by machine learning
    Requena, Borja
    Munoz-Gil, Gorka
    Lewenstein, Maciej
    Dunjko, Vedran
    Tura, Jordi
    PHYSICAL REVIEW RESEARCH, 2023, 5 (01):
  • [2] Many-Body Models for Molecular Nanomagnets
    Chiesa, A.
    Carretta, S.
    Santini, P.
    Amoretti, G.
    Pavarini, E.
    PHYSICAL REVIEW LETTERS, 2013, 110 (15)
  • [3] Automated machine learning of accurate many-body potentials for molecular simulations
    Bull-Vulpe, Ethan
    Ganapathy, Kaushik
    Riera, Marc
    Zhai, Yaoguang
    Paesani, Francesco
    Goetz, Andreas
    Brown, Sandra
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 258
  • [4] Automated machine learning of many-body potentials for accurate molecular simulations
    Reddy, Sandeep Kumar
    Thuong Nguyen
    Zhai, Yaoguang
    Goetz, Andreas
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 256
  • [5] Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
    Hansen, Katja
    Biegler, Franziska
    Ramakrishnan, Raghunathan
    Pronobis, Wiktor
    von Lilienfeld, O. Anatole
    Mueller, Klaus-Robert
    Tkatchenko, Alexandre
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2015, 6 (12): : 2326 - 2331
  • [6] Machine Learning for Many-Body Localization Transition
    饶文嘉
    Chinese Physics Letters, 2020, 37 (08) : 17 - 23
  • [7] Machine Learning for Many-Body Localization Transition*
    Rao, Wen-Jia
    CHINESE PHYSICS LETTERS, 2020, 37 (08)
  • [8] Machine learning approach to dynamical properties of quantum many-body systems
    Hendry, Douglas
    Feiguin, Adrian E.
    PHYSICAL REVIEW B, 2019, 100 (24)
  • [9] Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra
    Venturella, Christian
    Hillenbrand, Christopher
    Li, Jiachen
    Zhu, Tianyu
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 20 (01) : 143 - 154
  • [10] Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
    Pronobis, Wiktor
    Tkatchenko, Alexandre
    Mueller, Klaus-Robert
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2018, 14 (06) : 2991 - 3003
12345