| 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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| 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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