Materials design using first principles calculations and machine learning
| Project/Area Number |
19H02419
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 26010:Metallic material properties-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Seko Atsuto 京都大学, 工学研究科, 准教授 (10452319)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2021: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2020: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2019: ¥6,630,000 (Direct Cost: ¥5,100,000、Indirect Cost: ¥1,530,000)
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| Keywords | 機械学習 / 第一原理計算 / 結晶構造探索 / 原子間ポテンシャル / 構造探索 / 転移学習 / 材料インフォマティクス / 原子間ポテンシャル |
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| Outline of Research at the Start |
本研究は，最先端の機械学習を積極的に導入することにより，第一原理計算の多重実行に基づく高度な材料計算の方法論構築を行うものである．第一原理計算の多重実行に基づく高度な材料計算において本質的な課題である３つの研究項目（原子間ポテンシャル構築，最安定結晶構造探索，表現学習による記述子抽出）を設定し，第一原理計算に基づく現実的な材料計算を目指す．
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| Outline of Final Research Achievements |
The machine-learning potential (MLP) providing an accurate description of the relationship between the energy and the crystal structure and its potential applications are of growing interest. Such an approach is a framework of polynomial MLP, in which the introduction of group-theoretical high-order rotational polynomial invariants contributes to systematically derive MLPs with high predictive power for a wide range of structures, including extreme structures. This approach successfully constructs accurate and efficient MLPs in a variety of elemental metals and alloys. The Pareto optimal polynomial MLPs with different trade-offs between accuracy and computational efficiency for various systems are distributed in Polynomial Machine Learning Potential Repository with our implementation (polymlp-package) that enables us to use the polynomial MLPs in the LAMMPS code.
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| Academic Significance and Societal Importance of the Research Achievements |
基礎的な第一原理計算は，系の元素・結晶構造をもとに，エネルギーや電子状態を計算するものであり，材料研究に広く用いられている．しかし，実際の材料の物性や現象に対しては，非常に単純なモデルを導入し第一原理計算を行う以外なく，その精度を確かめる手段すらない．そのような状況において，本研究は，第一原理計算の精度で，実際の材料物性や現象を取り扱う方法を構築することで，材料研究を大幅に進展させるものである．
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Report
(4 results)
Research Products
(14 results)
