PhD position: Machine-learning-driven molecular simulation of mineral carbonation
- Post by: Xinping Zhu
- 9 November 2026
- No Comment
Context: Carbon mineralization, the conversion of CO2 into stable carbonate phases through reactions with calcium- and magnesium-bearing minerals, is attracting increasing interest for carbon management and the development of more sustainable construction materials. Despite significant progress at the macroscopic scale, the molecular mechanisms governing mineral dissolution, aqueous speciation, interfacial transport, and the early stages of carbonate formation remain incompletely understood. We are seeking a highly motivated PhD candidate to investigate the molecular-scale mechanisms controlling the carbonation reactivity of calcium- and magnesium-bearing silicate minerals in aqueous environments, with particular emphasis on wollastonite as a model calcium silicate mineral. The overall objective is to establish molecular-level relationships between interfacial structure, solution chemistry, mineral dissolution, and subsequent carbonation processes, with particular attention to how these processes are influenced by externally applied electric fields.
Objectives: In this project, the candidate will focus on the elementary physics and chemistry of the carbonation process of wollastonite under the applied external electric fields. The candidate will develop machine learning potentials and apply atomistic simulation approaches to investigate phenomena occurring at wollastonite-water interfaces, including hydration and molecular structure of mineral surfaces, adsorption and interfacial organization of water and aqueous species, dissolution and release of calcium and silicate species, evolution of surface chemistry during mineral-water interactions, ion transport and association near mineral surfaces, and molecular mechanisms relevant to carbonate nucleation and mineralization etc. The simulations will be closely connected with experimental observations obtained within the EXCARB project, providing opportunities to establish links between molecular-scale mechanisms and experimentally measurable quantities.
Methodology: The project will primarily rely on machine-learning interatomic potentials (MLIPs), molecular dynamics simulations, and enhanced-sampling techniques, complemented by quantum-mechanical calculations. The candidate will develop, train, and validate machine-learning interatomic potentials using reference data generated from first-principles calculations, particularly density functional theory (DFT). Particular attention will be given to accurately describing reactive mineral-water interfaces, including bond breaking and formation, proton transfer, ionic interactions, and the evolution of surface chemistry. The resulting potentials will be used to perform large-scale molecular dynamics simulations, extending the accessible spatial and temporal scales beyond those of conventional ab initio molecular dynamics. Advanced sampling techniques, such as metadynamics and umbrella sampling, complemented with machine learning collective-variables, will be employed to investigate rare events, identify reaction pathways, and quantify free-energy barriers associated with mineral dissolution, ionic association, and early carbonate formation. The candidate will have access to high-performance computing resources and will work in a multidisciplinary research environment combining molecular simulation, computational chemistry, interfacial physical chemistry, mineral science, and experimental characterization. Close interactions with experimental researchers will provide opportunities to validate simulation results and establish meaningful connections between atomistic mechanisms and macroscopic mineral carbonation behavior.
Candidate profile: We welcome applications from candidates with a Master’s degree or equivalent in one of the following or closely related fields: computational chemistry, computational material science, physical chemistry, chemical physics, geochemistry, and material science etc. are welcomed. A strong interest in molecular simulation and interfacial physical chemistry is essential. Previous experience with molecular dynamics, Monte Carlo methods, electronic-structure calculations, enhanced sampling, or scientific programming would be advantageous but is not mandatory. Experience with tools such as MACE, LAMMPS, CP2K, PLUMED, or equivalent simulation packages would be particularly valuable. Note that the candidate should be able to communicate in English.
About the position: This position is part of research project funded by ANR JCJC (French National Research Agency). The PhD student will be registered at the doctoral school of Institut Polytechnique de Paris (IP Paris), which ranks 43rd worldwide and 2nd in France in the QS World University Rankings 2027. It holds strong positions globally for science, technology, and graduate employability. We are looking for an outstanding PhD candidate to join our team and contribute to this research on carbonation.
Practical details:
- Research team: The selected candidate will join Laboratoire Navier, a multidisciplinary research laboratory working at the interface of mechanics, physics, materials science, and civil engineering. The PhD student will work with Xinping Zhu and Laurent Brochard at Laboratoire Navier. Romain Dupuis at Laboratoire de Mécanique et Génie Civil (LMGC), Simone Pezzotti at Ecole Normale Supérieure, and Antonino Marco Saitta at Laboratoire de Physique de l’ENS will also co-supervise the student.
- Salary: ∼2300 € gross per month.
- Location : 6 et 8 avenue Blaise- Pascal – Champs-sur-Marne, France. 20 minutes by RER train from Paris city center.
- Starting date: 1 October 2027
- Duration: 36 months
- Application deadline: 28 February 2027 (the deadline may be extended if the position remains unfilled)
How to apply?
Interested candidates should provide:
- A detailed CV;
- A motivation letter describing their research interests and relevant experience;
- Master’s/Bachelor’s transcripts;
- 2-3 referees
Please send your application files to Xinping ZHU by email (xinping.zhu@enpc.fr), Laurent Brochard (laurent.brochard@enpc.fr), Romain Dupuis (romain.dupuis@umontpellier.fr), Simone Pezzotti (simone.pezzotti@ens.psl.eu), and Antonino Marco Saitta (marco.saitta@phys.ens.fr). Please name your email subject in a format of “PhD MD application – <your full name>”.