M/F PhD in Theoretical Chemistry and Machine Learning

Organisation/Company CNRS Department Institut Charles Gerhardt Montpellier Research Field Chemistry Chemistry » Computational chemistry Researcher Profile First Stage Researcher (R1) Application Deadline 1 Jun 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The objectives of this PhD thesis are part of a broader collaborative research project dedicated to the discovery and understanding of new catalysts for more sustainable ammonia (NH₃) synthesis. Ammonia, thanks to its high energy density, represents a promising alternative for hydrogen storage, which remains limited for renewable energy systems. However, its current production via the Haber–Bosch process is highly energy‑intensive and CO₂‑emitting. The development of more decentralized and low‑temperature processes therefore requires new high‑performance catalysts. In this context, the project aims to identify new materials for both ammonia synthesis and decomposition by combining modelling approaches with rational design strategies.

Thesis Objectives

• Identify new stable and active catalysts for low‑temperature ammonia synthesis and decomposition, and understand structure‑reactivity relationships to guide optimisation.
• Analyse elementary reaction mechanisms and performance trends in catalytic materials, linking microscopic properties to overall activity.

Work Programme

• Explore new catalytic materials and identify stable and potentially active compositions among different classes of compounds, including transition metal‑based systems, while investigating the effect of catalyst supports.
• Carry out a detailed mechanistic study to understand the key steps of ammonia synthesis and decomposition, focusing on reaction pathways, intermediates, and transition states.
• Relate these mechanisms to catalytic performance and extract general trends to guide the design of new materials.

Required Skills

• Strong background in theoretical physics, theoretical chemistry, and mathematics.
• Experience in methodological development and implementation of methods in theoretical chemistry codes.

Department/Institute Presentation

The PhD student will benefit from the theoretical chemistry and programming expertise of researchers in Department D5 at the Charles Gerhardt Institute of Montpellier (ICGM), as well as the necessary environment to carry out calculations (local clusters, national computing centers – GENCI). Collaboration with project partners, particularly in the synthesis and characterization of catalysts, and DFT calculations of spectral signals, will also be essential.

Theoretical design and mechanistic study of low‑temperature NH₃ catalysts using machine learning and DFT