陣内亮典

物性予測に必要な原理的に正しい方程式は1928年にPaul Diracにより与えられました。しかし実際の燃料電池研究において、高性能な電極材料をこの方程式のみから見つけ出すことはできません。

 

私はこの方程式を出発点とし、工業的に有用な材料、例えば燃料電池を高性能化する電極・電解質材料を設計し、またこれらの材料を利用するデバイスの性能を予測できる、計算技術の構築を目指しています。
第一原理計算および分子シミュレーションを活用して、電極反応場における界面物質輸送抵抗の起源を明らかにしました。また、この計算の適用範囲を拡張できるよう、計算精度を維持しつつ計算速度を100-1000倍以上、高速化できる機械学習技術も開発しました。
電極・電解質内現象を統合的に取り扱えるように計算技術を拡充し、理想的な燃料電池の全体設計を実現します。

Jinnouchi, R., Karsai, F. and Kresse, G., “Machine Learning-aided First-principles Calculations of Redox Potentials”, npj Computational Materials, Vol. 10 (2024), 107.
https://doi.org/10.1038/s41524-024-01295-6

 

Jinnouchi, R., Minami, S., Karsai, F., Verdi, C. and Kresse, G., “Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential”, Journal of Physical Chemistry Letters, Vol. 14 (2023), pp. 3581-3588.
https://doi.org/10.1021/acs.jpclett.3c00293

 

Jinnouchi, R., “Molecular Dynamics Simulations of Proton Conducting Media Containing Phosphoric acid”, Physical Chemistry Chemical Physics, Vol. 24 (2022), pp. 15522-15531.
https://doi.org/10.1039/d2cp00484d

 

Jinnouchi, R., Kudo, K., Kodama, K., Kitano, N., Suzuki, T., Minami, S., Shinozaki, K., Hasegawa, N. and Shinohara, A., “The Role of Oxygen-permeable Ionomer for Polymer Electrolyte Fuel Cells”, Nature Communications, Vol. 12 (2021), 4956.
https://doi.org/10.1038/s41467-021-25301-3

 

Jinnouchi, R., Karsai, F., Verdi, C. and Kresse, G., “First-principles Hydration Free Energies of Oxygenated Species at Water-platinum Interfaces”, Journal of Chemical Physics, Vol. 154 (2021), 094107.
https://doi.org/10.1063/5.0036097

 

Jinnouchi, R., Karsai, F., Verdi, C., Asahi, R. and Kresse, G., “Descriptors Representing Two- and Three-body Atomic Distributions and Their Effects on the Accuracy of Machine-learned Inter-atomic Potentials”, Journal of Chemical Physics, Vol. 152 (2020), 234102
https://doi.org/10.1063/5.0009491

 

Jinnouchi, R., Karsai,F. and Kresse, G., “Making Free-energy Calculations Routine: Combining First Principles with Machine Learning”, Physical Review B, Vol. 101 (2020), 060201(R).
https://doi.org/10.1103/PhysRevB.101.060201

 

Jinnouchi, R., Miwa, K., Karsai, F., Kresse, G. and Asahi, R., “On-the-fly Active Learning of Interatomic Potentials for Large-scale Atomistic Simulations”, Journal of Physical Chemistry Letters, Vol. 11 (2020), pp. 6946–6955.
https://doi.org/10.1021/acs.jpclett.0c01061

 

Jinnouchi, R., Karsai, F. and Kresse, G., “On-the-fly Machine Learning Force Field Generation: Application to Melting Points”, Physical Review B, Vol. 100 (2019), 014105.
https://journals.aps.org/prb/pdf/10.1103/PhysRevB.100.014105

 

Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G. and Bokdam, M., “Phase Transitions of Hybrid Perovskites Simulated by Machine-learning Force Fields Trained on the Fly with Bayesian Inference”, Physical Review Letter, Vol. 122 (2019), 225701.
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.225701

 

Jinnouchi, R. and Asahi, R., “Predicting Catalytic Activity of Nanoparticles by a DFT-aided Machine-learning Algorithm”, Journal of Physical Chemistry Letters, Vol. 8 (2017), pp. 4279–4283.
https://doi.org/10.1021/acs.jpclett.7b02010