Frugal Machine Learning and Density Functional Theory for the Design of Sustainable Catalytic Materials (M/F)

Job Description

Thesis Subject

Scientific context : The catalytic conversion of carbon dioxide into methanol is widely recognized as a key route for carbon valorization and greenhouse gas mitigation. When coupled with renewable hydrogen, this reaction offers a promising pathway toward sustainable fuel production and long-term decarbonization of the chemical industry. In recent years, catalysts based on oxide–metal and oxide–intermetallic interfaces have emerged as particularly promising systems, as these interfaces can strongly influence CO₂ activation and methanol selectivity. However, the atomic-scale structure of these interfaces and the mechanisms governing their catalytic activity remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging.

Recent advances in machine learning approaches provide a powerful framework to model complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal scales than conventional electronic structure methods. However, these development typically requires very large training datasets generated from computationally expensive calculations, which represents a major bottleneck for the study of complex catalytic interfaces.

Objectives : The objective of the thesis is to develop data-efficient machine learning strategies for CO₂ hydrogenation to methanol, catalyzed by oxide-metal interfaces. Key ideas include the consideration of transfer learning, machine learning interaction potentials, and existing knowledge from experimental studies.

Techniques/methods in use: Density Functional Theory, Machine Learning

Applicant skills: Strong background in chemistry, physical chemistry, materials science, or condensed matter physics. Experience in data science, Python programming, high-performance computing and/or quantum chemistry will be considered an asset. Excellent communication skills are essential,
with the ability to work and exchange ideas effectively both orally and in writing. English speaking is required. The application should include a statement of research interest, a CV and Master's degree transcript.

Your Work Environment

The Institute Jean Lamour (IJL) is a joint research unit of CNRS and Université de Lorraine.
Focused on materials and processes science and engineering, it covers: materials, metallurgy, plasmas, surfaces, nanomaterials and electronics.
By 2026, IJL has 258 permanent staff (33 researchers, 133 teacher-researchers, 92 IT-BIATSS) and 389 non-permanent staff (146 doctoral students, 43 post-doctoral students / contractual researchers and more than 200 trainees), from some seventy different nationalities.
Partnerships exist with 150 companies and our research groups collaborate with more than XX countries throughout the world.
Its exceptional instrumental platforms are spread over 4 sites ; the main one is located on Artem campus in Nancy.
The thesis will take place within Research Group 102, "Plasmas, Processes, and Surfaces."

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

The research professions