Procedure for constructing machine learning interatomic potentials

2017 Fiscal Year Final Research Report

• Project/Area Number: 15H04116
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Physical properties of metals/Metal-base materials
• Research Institution: Kyoto University
• Principal Investigator: Seko Atsuto 京都大学, 工学研究科, 准教授 (10452319)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: 機械学習 / 原子間ポテンシャル / 分子動力学 / 回帰分析 / 第一原理計算

Outline of Final Research Achievements

Machine-learning interatomic potential (MLIP) has been of growing interest as a useful method to describe the energetics of systems of interest. In the present study, we examine the accuracy of linearized pairwise MLIPs and angular-dependent MLIPs for 31 elemental metals. Using all of the optimal MLIPs for 31 elemental metals, we show the robustness of the linearized frameworks, the general trend of the predictive power of MLIPs and the limitation of pairwise MLIPs. As a result, we obtain accurate MLIPs for all 31 elements using the same linearized framework. This indicates that the use of numerous descriptors is the most important practical feature for constructing MLIPs with high accuracy. An accurate MLIP can be constructed using only pairwise descriptors for most non-transition metals, whereas it is very important to consider angular-dependent descriptors when expressing interatomic interactions of transition metals.

Free Research Field

計算材料科学