MajuLab Seminar by Mathieu Salanne – 27 March 2024

Seminar: Wednesday, March 27, 2024
4:30 PM Singapore time / 9:30 AM French time

In person at the CQT level 5 seminar room & online via Zoom for the seminar. Registration is required.

Please register at:

Mathieu Salanne, Sorbonne Université, CNRS, Laboratoire PHENIX, Institut Universitaire de France, Paris, France


Mathieu Salanne

Mathieu Salanne is professor of chemistry at Sorbonne University. His research field of interest is the simulation of electrolytes for energy production and storage, with a focus on methodological developments for the modelling of electrochemical interfaces. His research has been recognized by the IUPAP young scientist prize in computational physics in 2014. In 2020 he was appointed as a junior member of Institut Universitaire de France.


Introducing quantum effects in classical simulations of electrochemical interfaces

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 that allows to simulate electrochemical cells [1]. 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), using a semi-classical Thomas-Fermi model [2], and for the surface polarization of 2D materials such as MoS2 [3]. In parallel, we have recently 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 [4], which opens the way towards the use of machine learning to predict the charge density response of the electrode with DFT accuracy [5].


[1] Marin-Laflèche, A. et al., J. Open Source Softw., 5 (2020), 2373

[2] Scalfi, L., Dufils, T., Reeves, K.G., Rotenberg, B., Salanne, M., J. Chem. Phys., 153 (2020), 174704

[3] Bi, S., Salanne, M. ACS Nano, 16, 18658 (2022)

[4] Dufils, T., Jeanmairet, G., Rotenberg, B., Sprik, M., Salanne, M. Phys. Rev. Lett., 123 (2019),195501

[5] Grisafi, A., Bussy, A., Salanne, M., Vuilleumier, R., Phys. Rev. Mater., 7 (2024), 125403

MajuLab is an international joint research unit of the CNRS, UCA, SU, NUS and NTU in Singapore (IRL 3654), hosted by CQT and SPMS.