2021 Fiscal Year Final Research Report
Development of Molecular Structure Search Method and Automated Parameter Construction Scheme Based on Machine Learning
| Project/Area Number |
20K22539
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
0501:Physical chemistry, functional solid state chemistry, organic chemistry, polymers, organic materials, biomolecular chemistry, and related fields
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| Research Institution | Waseda University |
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Principal Investigator |
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| Project Period (FY) |
2020-09-11 – 2022-03-31
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| Keywords | 量子化学計算 / 機械学習 / 構造最適化 |
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| Outline of Final Research Achievements |
In computational chemistry, the fast prediction of atom forces in molecules is essential in the rapid exploration of molecular structures, including chemical reactions. This study aimed to develop a fast prediction method of atom forces using machine learning. In this study, a database of atom forces related to geometry optimization of organic molecules and chemical reactions of the organometallic complex was constructed. The prediction accuracy was assessed by applying various machine learning methods. The knowledge about databases, descriptors, and machine learning methods for predicting atom forces was obtained. The constructed database contains information about a large number of non-equilibrium molecular structures. It is usable for a wide range of computational chemistry research.
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| Free Research Field |
理論化学、ケモインフォマティクス
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、機械学習を用いたポテンシャルの予測において今日広く用いらている手法と異なり、原子のフォースを直接予測する点で特異である。これに有効な記述子や機械学習手法を検証した点は学術的な意義がある。また、本手法の精度をさらに向上させることで分子構造の迅速な探索が可能となれば、新規化合物の設計など、計算化学分野で広く行われている研究課題に対して貢献することも期待される。
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