Biomass-based sugars are a key feedstock for the sustainable manufacture of platform chemicals. Zeolites having Sn cations in their framework have proven efficient for the catalysis of several important sugar transformation reactions. Available static density functional theory (DFT) calculations suggest several modes of interactions between sugars and the potential active sites. However, they cannot capture the dynamics, confinement and explicit solvent effects that govern sugar/Sn-site interactions in zeolite pores. To address those limitations of the static approach, one can resort to molecular dynamics. To this end, the postdoctoral fellow will develop machine-learning interatomic potentials (MLIPs) to model sugar adsorption and reactivity in Sn-zeolites under hydrated conditions. Solid-state nuclear magnetic resonance (NMR) stands out as one of the most powerful tools to investigate molecular uptake and interactions at pore surfaces in zeolites, thus a focus will be put on the computation of relevant NMR features to be compared with experiments.

The work will be carried out at IFP Energies nouvelles (Solaize) in close collaboration with Charles University (Prague) and the CRMN (Villeurbanne) within the project MAINS funded by Agence Nationale de la Recherche (ANR). The PhD degree must have been awarded no more than three years before the start of the postdoctoral contract.

The post-doctoral fellow will (i) build reference ab initio datasets (periodic calculations) for {Sn-zeolite/sugar/water} systems; (ii) fine-tune foundation MACE models and run ns-scale MLIP molecular dynamics to screen open/closed/hydrated Sn sites, sugar tautomers, adsorption modes and hydration levels; (iii) train ML predictors of 13C and 119Sn NMR chemical shifts to compute dynamically averaged spectra, to be compared with experimental DNP NMR data obtained at our partner CRMN in Lyon; and (iv) contribute to structure–activity relationships for glucose isomerization and retro-aldol reactions. He/She will be in charge of regular literature survey on the topic, of regular reporting to the project team, of presenting talks at international conferences, and writing drafts of papers. He/She will participate to the computational/experimental synergy within the project, by discussing possible interpretation of experimental results acquired at CRMN.

Short stays at our collaborators at Charles University, Prague, Czech Republic

Frequent visits at our collaborators at CRMN, Villeurbanne, France (close to Lyon)

Possible home-office (from France) part of the time

Compétences techniques et aptitudes 

Knowledge in chemistry and materials science, with experience in DFT and/or ML potentials; knowledge of zeolites or catalysis is a plus.

Ability to work with several research teams, with autonomy and high scientific rigor and ethics.

Langues 

English : at least B2 level

Candidates should hold a PhD in computational chemistry or computational materials science

For further information and to apply, please contact:

celine.chizallet@ifpen.fr