Group presentation

The MPC team conducts multidisciplinary research focused on the modeling, characterization, and understanding of molecular systems, materials, and complex media — including those of biological interest — as well as nanomaterials. A unifying feature across the team is its strong expertise in electronic structure calculations based on Density Functional Theory (DFT). This expertise is complemented by skills in molecular dynamics and force field optimization, as well as in advanced GW calculations, which enable access to excited-state electronic properties with higher accuracy than DFT.

The team thus develops a comprehensive theoretical and physico-chemical approach founded on DFT modeling, coherently addressing electronic structure, thermodynamic stability, kinetic parameters, and spectroscopic properties. In close collaboration with experimental teams, it strives to provide theoretical insights that foster a deep and constructive dialogue between theory and experiment. Spectroscopic simulations play a central role, serving as an essential tool for direct comparison with experimental data. This strong link between theory and experiment forms a cornerstone of the MPC team’s scientific background and expertise.

More recently, the theme of artificial intelligence applied to chemistry has undergone significant development within the team. We are particularly exploring the contribution of machine learning methods to property prediction, compound classification, in silico design, and the analysis of experimental data. These studies reflect a commitment to developing cross-disciplinary approaches that combine modeling, data science, and AI, with the aim of enhancing the predictive power of models and deepening the theory–experiment dialogue. The rapidly growing “AI” research line within the MPC team now represents a strategic focus shaping the future of its scientific activities. AI has not made us lose our soul, as we almost systematically integrate eXplainable Artificial Intelligence (XAI) into our work. XAI brings transparency to deep learning models by revealing the molecular factors that drive their predictions. In chemistry, it bridges data-driven modeling and mechanistic understanding, turning black-box models into tools for reasoning and discovery. By linking descriptors to structure–property relationships, XAI strengthens confidence in AI-assisted decision making. The rapidly growing “AI” research line within the MPC team is now a strategic direction for the evolution of its research activities.