Defect Behavior in Mg Alloys with High-accuracy Machine-learning Interatomic Potentials

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K04175
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 18010:Mechanics of materials and materials-related
• Research Institution: Shinshu University
• Principal Investigator: Matsunaka Daisuke 信州大学, 学術研究院工学系, 教授 (60403151)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 機械学習ポテンシャル / 人工ニューラルネットワーク / マグネシウム合金 / 底面転位 / 合金元素

Outline of Final Research Achievements

Magnesium has been of increasingly interest as a promising light structural material because of its low density. Plastic deformation in Mg is contributed by non-basal slips and twinning as well as basal slip. Effects of alloying elements on these deformation modes are reflected to mechanical properties in Mg alloys. In this study, high-accuracy interatomic potentials of Mg alloys based on artificial neural network (ANN) have been developed by using machine learning techniques. The ANN interatomic potentials can describe various defects corresponding to the deformation modes in a hcp structure and interactions between Mg and alloying element atoms. By using the developed ANN interatomic potentials, behaviors of the defects and effects of alloying elements have been analyzed.

Free Research Field

計算材料科学

Academic Significance and Societal Importance of the Research Achievements

固体材料の力学的挙動の解析に原子系のシミュレーションが用いられるようになってから現在に至るまで，精度の良い原子間ポテンシャルの開発は重要な課題であり続けている． ANN原子間ポテンシャルに関しては，近年注目を集めているが，結晶性固体において弾性場と欠陥構造を精度よく表現することを志向した開発例は未だ少ない．本研究はこのような研究動向に先駆けて，ANN原子間ポテンシャルの構築技術を確立するものであり，Mg合金の研究を格段に進めるだけでなく，計算力学・計算科学の分野におけるインパクトは非常に高い．