Artificial neural network for the configuration problem in solids

被引：10

作者：

Ji, Hyunjun ^{[1
]}

Jung, Yousung ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Grad Sch EEWS, 291 Daehak Ro, Daejeon 34141, South Korea

来源：

JOURNAL OF CHEMICAL PHYSICS | 2017年 / 146卷 / 06期

基金：

新加坡国家研究基金会;

关键词：

POTENTIAL-ENERGY SURFACES; MOLECULAR-MECHANICS; MOVTENBO CATALYSTS; FORCE-FIELD; PHASE; APPROXIMATION; CHEMISTRY; ACCURATE; STORAGE; M1;

D O I：

10.1063/1.4974928

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R-2 = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R-2 = 0.95 and MAD = 0.13 eV. Published by AIP Publishing.

引用

页数：11

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