Missions

In this project, which is part of the PostGenAI Cluster led by Sorbonne Université, the post-doc will model three solid-liquid interfaces where reactivity is important:

1) Electrode-electrolyte interface, at ambient pressure and temperature. The aim will be to model the effect of an electric field on reactivity in the liquid, in order to quantify the main chemical reactions leading to the formation, by precipitation, of constituent phases of the solid electrolyte interphase (electrochemical energy storage application);

2) Diamond-CH4/H2O mixture interface, at high pressure and temperature. Here, we will be looking for conditions at which diamond is the thermodynamically stable phase, in order to study the growth process of the solid phase. These interfaces are typically found in planetary interiors, such as in icy giants (Uranus, Neptune);

3) Graphene electrode-carbon-rich liquid interface, at ambient pressure and elevated temperature. The aim is to assess whether, in the presence of an electric field, it is possible to form hydrocarbons from a carbon-rich liquid (a water/methanol mixture, for example), by a superacid route.

Activités

The post-doc will contribute to various aspects of the project, such as:
- developing new theoretical and numerical approaches for determining the thermodynamics associated with a transformation, using artificial intelligence tools;
- carrying out molecular dynamics simulations;
- development of codes to analyze these simulations, based on existing tools at the lab;
- collaboration with other project participants;
- writing publications and taking part in conferences.

Compétences

- Skills/knowledge: statistical physics, machine learning, physical chemistry;
- Know-how: molecular simulations, programming experience (python, C++, julia, C, ...), python programming for artificial intelligence (pytorch, tensorflow, ...);
- People skills: teamwork

Contexte de travail

The post-doc will participate in the IA-Cluster (ANR) PostGenAI project, led by Sorbonne Université, and more specifically in the “Ai-Augmented Multiscale Modeling for Energy Storage” sub-project, which aims to develop artificial intelligence methods to complement modeling techniques, particularly at the atomic scale, specific to the physical chemistry of materials.