Data science-assisted mesoscale microstructural prediction method for developing high-performance materials via grain boundary engineering

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K14140
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 18010:Mechanics of materials and materials-related
• Research Institution: Osaka Metropolitan University (2023); Tokyo University of Agriculture and Technology (2022)
• Principal Investigator: Miyoshi Eisuke 大阪公立大学, 大学院工学研究科, 講師 (70880962)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: 微視組織 / 粒界 / 粒界多重点 / 焼鈍 / フェーズフィールド法 / 分子動力学 / データ同化

Outline of Final Research Achievements

To achieve a quantitative prediction of grain boundary network evolution during annealing processes, this study presented a trans-scale analysis method that quantitatively integrates atomic-scale simulations with continuum-scale phase-field simulations through data assimilation. The present method enabled anisotropic (inclination-dependent) grain boundary properties, which are difficult to measure experimentally, to be efficiently estimated for various types of grain boundary structures, establishing a promising basis for highly accurate precision of annealing phenomena. Furthermore, we developed a novel phase-field model capable of representing the properties of grain boundary multi-junctions and realized the evaluation of the multi-junction properties through data assimilation.

Free Research Field

計算材料学

Academic Significance and Societal Importance of the Research Achievements

焼鈍過程における高特性粒界（低Σ値粒界や低エネルギーのファセット粒界など）の増加を利用した材料の高機能化，すなわち粒界工学の技術は，合金化に依らない省資源型材料開発において重要な役割を担う．本研究は，原子計算，連続体モデル，データ科学の融合により，粒界組織制御に不可欠でありながら従来欠落していた異方的粒界物性値や多重点物性値の情報を容易に取得可能とした．これらは，粒界工学の高度化による材料開発加速に資する基盤的枠組を示すものであり，学術・産業両面での貢献が期待できる．