I earned my PhD in Physcial Chemistry in 2020 from Université de Sherbrooke, Québec, Canada. I then joined CEA-Le Ripault to work on the AI-driven design of new organic molecules. By doing so, I got interested in many chemistry-related tasks that can be tackled through machine learning algorithms, from molecular property prediction and generative models embedding synthetic feasibility, to the analysis of experimental data for optimizing reaction conditions. In 2025, I became part of the LMCE at CEA DAM Île-de-France. Here, I expanded my research to include the use of machine learning for enhancing molecular dynamics simulations. By integrating physics-based and data-driven approaches, my work aims to deepen our understanding of chemical reactivity in condensed phase from different perspectives, ultimately supporting chemists in the efficient design of novel functional molecules.