Chemistry of Metals and Complex Materials. The team stands out for its strong activity in organometallic chemistry, the chemistry of lanthanides, and, more uniquely, of actinides, as well as in surface reactivity related to single-atom catalysis. Reaction mechanisms, studies of carbides, hydrides, multinuclear complexes, and surface functionalization are investigated through approaches combining high-level theoretical calculations (DFT) with advanced experimental characterization. These studies, for example, make it possible to identify reaction intermediates that are invisible experimentally, or to rationalize trends observed in catalysis through subtle electronic effects. The team’s expertise in actinide chemistry is rare at both the national and international levels, and it significantly strengthens its scientific identity.
Nanostructured Materials and Interfaces. Several research efforts focus on the reactivity of nanoparticles — notably of ruthenium and palladium — on two-dimensional materials (graphene, transition-metal dichalcogenides), and on molecule/surface interfacial interactions. Phenomena of structuring at the nanometric scale and their impact on electronic and catalytic properties are studied in detail. This area naturally represents a strong axis of collaboration with the NCO team of LPCNO.
Bio-Inspired Physical Chemistry and Complex Media. This interdisciplinary axis addresses self-assembly, noncovalent interactions, and conformational dynamics in bioinorganic or hybrid systems. These studies also explore the structural stability of such systems under various experimental conditions. They rely on tailored force fields and extensive molecular dynamics simulations, enabling the reproduction of realistic environments (solvents, solid interfaces, pH, etc.).
Data Science and Artificial Intelligence Applied to Chemistry. More recently, the team has initiated research integrating machine learning methods for the prediction of physicochemical properties (eutectic mixtures, viscosity, interactions). This orientation opens new perspectives for materials screening and for the understanding of complex phenomena. In the long term, artificial intelligence could play a unifying role similar to that currently played by DFT in structuring the team’s scientific activity. A key contribution of this approach lies in the use of descriptors derived from electronic-structure calculations to feed interpretable predictive models, thus facilitating the virtual exploration of formulation spaces. In an extremely competitive field, the distinctive feature of our approach is the introduction of explainability (XAI), that is, the use of algorithms designed to understand and interpret how AI models operate and make decisions. The AI theme is currently strengthening collaborations between the NCO and MPC teams.
Excited Electronic Properties and Advanced Modeling. GW-type calculations represent a relatively rare expertise. They enable a detailed description of excited-state electronic properties (band gaps, excitons) in 2D materials and nanostructures, and provide decisive insights into the understanding of optoelectronic phenomena. This research area, positioned at the interface between chemistry and physics, forms a strong collaborative axis between the OPTO and MPC teams.
