2021 Fiscal Year Final Research Report
Integration of computational chemistry and machine learning for quantitative prediction of microscopic properties of polymers
| Project/Area Number |
19K05372
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 32010:Fundamental physical chemistry-related
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| Research Institution | The Institute of Statistical Mathematics (2021)
Shiga University (2019-2020) |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | 分子シミュレーション / 分子動力学計算 / 密度汎関数理論計算 / ラジカル重合 / ビニルポリマー |
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| Outline of Final Research Achievements |
We developed a simulation method for polymerization process that can reproduce microscopic properties such as tacticity of product polymers. We developed a program for the Red Moon method, which combines molecular dynamics simulation and Monte Carlo methods to construct complex systems generated by multiple types and iterations of chemical reactions.
Furthermore, by analyzing the behavior of guest molecules in the nanochannels of metal organic frameworks, we succeeded in obtaining molecular-level insight into the precision polymerization of polymers carried out in the nanochannels .
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| Free Research Field |
物理化学
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| Academic Significance and Societal Importance of the Research Achievements |
分子シミュレーション技法の活用により、高分子重合反応シミュレーションを実施することにより、実験データを直接参考にすることなく、得られる高分子のミクロ物性を予測可能な技法の開発に成功した。本手法を活用することで温度などの熱力学条件や、ナノ空間の制限空間内での重合など、種々の条件での重合をシミュレート可能となり、さらなる精密重合の実現へとつながるものである。
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