Authors: Ryosuke Jinnouchi | Saori Minami
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Keynote)</i> Advancing Electrochemistry with Kernel-Based Machine Learning Force Fields Ionics and charge transfer reactions in electrolytes and electrolyte-electrode interfaces are key phenomena in batteries, fuel cells, and electrolyzers. Advancing the frontiers of both the science and engineering of these electrochemical systems requires a deep understanding of atomic-scale mechanisms and the discovery of hidden rules in experimental observations. First-principles (FP) calculations are a powerful tool for obtaining atomic-scale insights. However, due to their high computational cost, predicting the finite-temperature properties of electrochemical systems involving the slow, nanosecond-scale reorganization of ions and solvents in electrolytes remains challenging. As a result, most simulations rely either on metastable and saddle-point structures on potential energy surfaces (PESs) or on molecular dynamics (MD) simulations on the 10–100 ps timescale. Recently, machine-learning force fields (MLFFs) have gained attention as a promising approach to extend the applicability of FP calculations. MLFFs can accurately reproduce PESs obtained from FP calculations, capturing not only a wide range of chemical bonds—including covalent, ionic, and metallic bonds—but also intermolecular interactions such as dispersion forces, electrostatic interactions, and H-bonding. In this presentation, we introduce the application of kernel-based MLFFs [1] to electrochemical phenomena, specifically hydrous and anhydrous proton transport in perfluorinated ionomers [2] and phosphoric and phosphonic acid electrolytes [3,4], as well as electrochemical reactions in homogeneous solutions [5,6] and at the water-Pt interface [7]. Our results demonstrate that MLFF-based finite-temperature MD simulations can successfully reproduce proton diffusion coefficients [2-4] and redox potentials of electrochemical reactions [5,6] in good agreement with experimental data. Furthermore, these simulations reveal the critical roles of H-bond networks in both hydrous and anhydrous proton transport, as well as in the interfacial oxygen reduction reaction (ORR) on Pt surfaces in the presence of organic molecules such as melamine. For example, in hydrous proton conduction, an increased connectivity of H-bonds between water molecules facilitates proton transport [2], whereas in anhydrous conduction, the recombination of H-bonds connecting anions is the rate-determining step for proton transport [3,4]. In the ORR at the water-Pt interface, the removal process of the reaction intermediate OH slows down when it is strongly stabilized by H-bonding, whereas the introduction of organic molecules such as melamine disrupts H-bonding and enhances catalytic activity [7]. MLFFs enable the evaluation of H-bond rearrangements on the nanosecond timescale with sufficient statistical accuracy, providing new insights into the universal importance of atomic-scale hydrogen-bond control. Acknowledgement: This work partially utilized the computational resources of the Fugaku supercomputer via the HPCI System Research Project (Project hp240022). The authors also acknowledge valuable advice on computational methods from Professor Georg Kresse and Dr. Ferenc Karsai. [1] R. Jinnouchi, F. Karsai, and G. Kresse Phys. Rev. B 100 014105 (2019). [2] R Jinnouchi, S Minami, F Karsai, C Verdi, G Kresse J. Phys. Chem. Lett. 14 3581 (2023). [3] R Jinnouchi Phys. Chem. Chem. Phys. 24 15522 (2022). [4] S Minami and R Jinnouchi J. Mater. Chem. A 11 , 16104 (2023). [5] R Jinnouchi, F Karsai, G Kresse npj Comput. Mater. 10 , 107 (2024). [6] R Jinnouchi, F Karsai, G Kresse Chem. Sci. 16 2335 (2025). [7] R Jinnouchi and S Minami J. Phys. Chem. Lett. 16 265 (2025). Electrochemistry ; Anhydrous ; Electrolyte ; Metastability ; Proton ; Intermolecular force ; Molecular dynamics ; Ion ; Electrostatics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Physical and Chemical Molecular Interactions