| Project/Area Number |
15K14122
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Inorganic materials/Physical properties
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| Research Institution | Nagoya University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | 原子間ポテンシャル / ニューラルネットワーク / 第一原理計算 / シミュレーション / 格子欠陥 |
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| Outline of Final Research Achievements |
In this study, we tried to develop atomic neural network potentials, to realize atomistic simulations of ceramic materials with higher accuracy than ever. In order to optimize weights and biases in the neural network, we used the conjugant gradient method. At the same time, we calculated total energies of reference materials by first-principles calculations to generate training and test data. It was found that the conjugate gradient method can efficiently reduce mean square errors and yet cannot achieve a total-energy accuracy of mean square error of 10 meV/atom. Then we also applied the simulated annealing method combined with the conjugate gradient method, and finally attained a total-energy accuracy of less than 10 meV/atom.
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