2026年3月5日、Dr. Chao-Ping (Cherri) Hsu,(Institute of Chemistry, Academia Sinica, Taipei, Taiwan)によるご講演が第188回構材ゼミ「From fundamental understanding through atomistic simulations to hypersonic technology development II」として開催されます。
日時：2026年3月5日（木曜日）15：00-16：00
場所：NIMS千現地区 先進構造材料研究棟5階カンファレンスルーム
講演題目：Machine Learning aided charge transport dynamics
講演者：Dr. Chao-Ping (Cherri) Hsu, Institute of Chemistry, Academia Sinica, Taipei, Taiwan
開催者：佐原 亮二（計算構造材料グループ）
Abstract :
Electron transfer couplings, or, the off-diagonal Hamiltonian element in diabatic state representations, are commonly used in characterizing electron transfer rate. It is also an important parameter in polaron models. Electronic couplings are sensitive to molecular geometries, especially in the intermolecular degrees of freedoms, and thus characterizing such nuclear dependency is important for charge-transport dynamics. We developed novel ML approaches for evaluating electronic coupling.[1,2,3] These ML models enabled us to investigate the spectral density function of the off-diagonal term, revealing both sub-Ohmic behavior and temperature dependence.[4] Additionally, we examined the outer-sphere reorganization energies in nonpolar systems, which significantly influence charge transfer activation energies. Although traditional dielectric polarization theories predicted negligible outer-sphere reorganization energy, our findings demonstrate substantial contributions to this parameter.[5] This discovery suggests that charge-transfer processes in typical nonpolar or weakly polar materials experience greater fluctuations than previously theorized models indicated. Our findings advance the fundamental understanding of charge transfer dynamics and challenge existing models in the field.
References
[1] Wang, C.-I., Braza, M. K. E., Claudio, G. C., Nellas, R. B., & Hsu, C.-P. Machine Learning for Predicting Electron Transfer Coupling. J. Phys. Chem. A, 2019,123(36), 7792–7802.
[2] Wang, C.-I., Joanito, I., Lan, C.-F., & Hsu, C.-P. Artificial neural networks for predicting charge transfer coupling. J. Chem. Phys., 2020, 153(21), 214113.
[3] Lin, H.-H.; Wang, C.-I.; Yang, C.-H.; Secario, M. K.; Hsu, C.-P. Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data. J. Phys. Chem. A 2024, 128 (1), 271–280.
[4] Wang, Y.-S.; Wang, C.-I.; Yang, C.-H.; Hsu, C.-P. Machine-Learned Dynamic Disorder of Electron Transfer Coupling. The Journal of Chemical Physics 2023, 159 (3), 034103.
[5] Yang, C.-H.; Wang, C.-I.; Wang, Y.-S.; Hsu, C.-P. Non-Negligible Outer-Shell Reorganization Energy for Charge Transfer in