PhD (M/F) Investigation of the H2 adsorption capactity by MXenes through atomistic simulations and Machine Learning

Job Description

Thesis Subject

This project aims to identify and optimize MXene structures for hydrogen storage by combining theoretical and computational approaches. MXenes, composed of stacked layers with the formula T-M-X-(M-X)-M-T', where M is a transition metal (such as titanium, vanadium, or niobium), X is carbon or nitrogen, and T/T' are functional groups (fluorine, oxygen, hydroxyl, etc.), offer immense potential with approximately 23,000 possible combinations. To narrow down this list, researchers are focusing on metals with high hydrogen affinity (Ti, V, Nb, Zr), while excluding those that are unstable or too expensive (such as chromium, hafnium, or scandium). By prioritizing experimentally viable structural phases and simplifying the synthesis (for example, by imposing T = T'). Once these structures have been selected and optimized using density functional theory (DFT) calculations, their hydrogen adsorption properties will be evaluated in terms of structures and energies.
Two mechanisms are being studied: physisorption, which is stabilized by van der Waals forces and characterized by a weak interaction without charge transfer, and chemisorption, which involves electron transfer between hydrogen and the metal, potentially leading to the dissociation of the H₂ molecule and the formation of hydrides. The project aims to develop neural network atomic potential (NNAP) models to dynamically and reactively simulate hydrogen adsorption on MXenes. Unlike traditional methods, which require large datasets, this approach uses machine learning. The goal is to create models capable of predicting not only adsorption, but also H₂ dissociation and hydride formation, by reusing data across different classes of MXenes to accelerate screening.

Your Work Environment

The work will be carried out at the Lumière Matière Institute in Lyon within the Theoretical Physical Chemistry team. The Ph.D. candidate (M/F) will be supervised by Pierre Mignon (Assistant Professor) and Colin Bousige (Research Associate) from the Multimaterials and Interfaces Laboratory. This project is part of an ANR project in collaboration with experimental teams. Results will be shared with these experimental teams on a regular basis to facilitate the synthesis of materials and assess their potential for application. The candidate (M/F) will actively participate in team activities (presenting results, supervising Master's students) and will have the opportunity to present their work at national and international conferences.

Constraints and risks

No issues

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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