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报告题目：Molecular dynamics simulations of electrochemical interfaces: physics-based models vs machine-learning approaches
报告时间：2026-03-18 10:00
报告人： Prof. Mathieu Salanne
Sorbonne University
报告地点：卢嘉锡楼202报告厅
转播地点：翔安校区能源材料大楼3号楼会议室3，漳州校区生化主楼307教室
报告摘要：
Applied electrochemistry plays a key role in many technologies, such as batteries, fuel cells, supercapacitors or solar cells. It is therefore at the core of many research programs all over the world. Yet, fundamental electrochemical investigations remain scarce. In particular, electrochemistry is among the fields for which the gap between theory and experiment is the largest. From the computational point of view, this is due to the difficulty of combining a realistic representation of the electrode electronic structure and of the electrolyte structure and dynamics. Over the past decade we have developed a classical molecular dynamics code, MetalWalls, that allows to simulate electrochemical cells. In a first step, the electrodes were modeled as perfectly screening metals with a constant applied potential between them. Recently, we have extended this approach in order to account for the degree of metallicity of the electrode (i.e. from semimetals to perfect conductors), which can be parameterized through the knowledge of the material electronic density of states. In parallel, we have shown that it is possible to replace the constant applied potential method by using the finite field method to a system with a slab geometry, which opens the way towards the use of machine learning to predict the charge density response of the electrode with DFT accuracy.
报告人简介：
Mathieu Salanne is professor of chemistry at Sorbonne University. His research field is the simulation of electrolytes for energy production and storage, with a focus on electrochemical interfaces. He obtained his PhD in 2006 and was appointed assistant professor at Sorbonne University in 2007 and promoted to full professor in 2016. He was group leader (ionic liquids and electrochemistry) at the PHENIX laboratory from 2014 to 2021, and was appointed as director of the Institute for computing and data science from 2022 to 2024. He also held am excellence chair in high-performance computing at Paris-Saclay University from 2014 to 2018. His research has been recognized by the IUPAP young scientist prize in computational physics in 2014, and he obtained an ERC consolidator grant in 2017 (AMPERE project). In 2020 he was appointed as a junior member of Institut Universitaire de France. He was member of the Editorial Advisory Board of the Journal of Chemical Physics (2020-2022), and currently serves as an Executive Editor for ACS Nano.
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