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2022 Fiscal Year Final Research Report

Development and validation of quantum physics and chemistry-interpretable deep learning methods in data-driven science

Research Project

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Project/Area Number 20K19876
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionNational Institute of Advanced Industrial Science and Technology

Principal Investigator

Tsubaki Masashi  国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (80803874)

Project Period (FY) 2020-04-01 – 2023-03-31
Keywords深層学習 / 密度汎関数理論
Outline of Final Research Achievements

The three main research results are as follows. First, we implemented the new deep learning model described above and showed that it can extrapolate and predict molecular energies within a certain error range. In the process of implementing this model, we also succeeded in mathematically demonstrating that the conventional deep learning model is equivalent to the superposition of wave functions in quantum chemical calculations. Furthermore, the new deep learning model was trained on a simple small molecule, and the learned model was transferred to the prediction of properties of more complex polymers. A total of three papers, one for each of these research results, were published in international journals.

Free Research Field

機械学習

Academic Significance and Societal Importance of the Research Achievements

学術的意義は、物理化学と機械学習という二つの分野を適切に融合できたことである。分子データを扱う際には、どちらかの分野の理論やアプローチのみに偏ることなく、それぞれの分野の良い部分をうまくミックスさせることが必要不可欠であり、それを達成することができた。また社会的意義は、分子データは製薬企業や材料企業のすべてが密接に関わるデータであり、その分野の研究者や技術者にとっての基礎技術を開発できたことである。学習モデルは、製薬企業や材料企業が独自に持つデータでも再学習可能であり、広く使われることが期待できる。

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Published: 2024-01-30  

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