Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.338
Publication date: 28th August 2024
The complexity in modeling electrochemistry processes in operando is multi-dimensionals. It spans from the understanding of fundamental reaction mechanisms at the atomic and molecular levels to capturing the dynamic interplay of mass transport, charge transfer, and interfacial phenomena. This complexity is further compounded by the need to integrate multiple time and spatial scales, from the rapid kinetics of electron transfer events to the slower diffusion processes and the changes in material structure and composition over extended periods of operation. processes in operando is multi-dimensionals. It spans from understanding. In this talk I will showcase personal examples of how machine learning modeling enables to account for and resolve structure-property relationship [1] and complex reactive dynamics [2].
[1] K. Rossi, G.G. Asara, F. Baletto - Acs Catalysis 10 (6), 3911-3920, 2020; S Zinzani, F. Baletto, K. Rossi - in writing
[2] C Zeni, K Rossi, et al - Nature Communications 12 (1), 6056, 2021; A. Grisafi, K. Rossi - in writing
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