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Portail > Offres > Offre UMR5253-MARDOU-006 - H/F PostDoc Conception assistée par IA d'électrolytes liquides pour batteries Na-ion

H/F PostDoc AI-assisted design of Na-ion electrolytes

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : lundi 30 janvier 2023

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General information

Reference : UMR5253-MARDOU-006
Nombre de Postes : 1
Date of publication : Monday, January 9, 2023
Type of Contract : FTC Scientist
Contract Period : 24 months
Expected date of employment : 15 February 2023
Proportion of work : Full time
Remuneration : from 2830€ per month (before taxes) - Adjustable according to experience
Desired level of education : 5-year university degree
Experience required : 1 to 4 years


- Collaborative work with members of the RS2E (french network on electrochemical energy storage) and more specifically with people involved in the BATMAN
project of the PEPR Batteries
- Local coordination (meeting organization, training of PhD, Master styudents ...)
- Development of an efficient computational strategy in link with the AI tools


DFT and MD calculations of electrolytes (bulk, surfaces) and electrolyte/electrode interfaces.
Creation of a database for AI
Writing of propossal / reports


PhD in computational material science
Knowledge in DFT, MD methods and solid state electronic structure

Work Context

This project falls in the frame of the RS2E network and is funded by the ANR through the PEPR Batteries

Constraints and risks

-No risk except those link with computer work

Additional Information

Over the past decade, many efforts were put on the development of electrolytes for high power NIBs. The main strategy consists in starting from successful electrolytes from LIBs, and find the best additives (and the necessary concentrations) for controlling the formation of a good SEI at the surface of the anode. This approach involves a lot of trial-and-error steps, which could be avoided through the use of AI. Indeed, the optimization problem is largely multidimensional since there are many different combination of additives available, and the ranking of the performances also depends on several criterion (capacity retention after an arbitrary number of cycles, cell resistance upon cycling, etc). In this project we will therefore gather the existing data (several tenth of systems were for example systematically tested) and our results and set up such an optimization. Although it is difficult to rationalize the results based on physical concepts, it is likely that the redox reactivity of the additives will play an important role. We will therefore supplement the experimental database with high-throughput calculations of electronic structure-based descriptors, such as the HOMO/LUMO positions, the solvation number, the solvation free energy, etc. The calculations will be made using the coupled electronic DFT/MDFT approach.

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