This class is taught together with Damien Laage. In the continuity of their first year at ENS (third and last year of their Bachelor's degree), the students expand their knowledge about important concepts in stat. mech. and the foundations of molecular simulations. In particular, I cover some advanced notions of classical mechanics, as well as diffusion processes (random walk, Langevin equation, generalized Langevin equation, Fokker-Planck equation and its applications to chemical reactivity such as Kramers' theory of the rate constant). This class includes practical hands-on sessions where the students learn to launch and to analyze their first molecular dynamics simulations.
This class is taught together with Philippe Nghe, Charlie Gosse, Zoher Guéroui, and Antoine Taly and Fabien Ferrage. In this class, we focus on the often overlooked dynamical aspects, such as conformational dynamics or transport phenomena, that govern the function of biological macromolecules. We give the students a comprehensive overview of the relevant timescales, and of the available experimental and simulation techniques to probe these processes, with a focus on the synergy between experiments, simulations, and theory. These concepts are also applied to practical cases selected from the literature but also to small numerical projects led by the students. Here, I cover important notions of stochastic processes, including random walks and Markovian dynamics, master equations, and their application to e.g. chemical reactivity or conformational exchanges.
This class is taught together with Élise Duboué Dijon, Fabio Sterpone and Davide Avagliano. Building upon core concepts of classical mechanics, statistical thermodynamics, and quantum chemistry, this class provides a large overview of the modern molecular simulation techniques that can be used to study chemical, biochemical and biophysical processes.
• Classical mechanics, equations of motion, propagators
• Simulation ensembles and thermodynamic conditions (thermostat, barostat)
• Enhanced sampling techniques (Umbrella Sampling, Metadynamics, Replica Exchange, Steered MD, etc.)
• Mixed quantum/classical approaches: concept, applications
• Beyond the molecular scale: coarse grained techniques, mesoscale techniques (eg Brownian dynamics, Dissipative Particle Dynamics, Lattice Boltzmann MD), principle of multi-scaling.
The core concepts of the class are demonstrated through various examples and applications, including enzymatic reactivity, conformational sampling, and protein diffusion in crowded environments. Students gain practical experience through hands-on sessions, where they learn to use popular molecular dynamics (MD) engines and analyze simulation trajectories effectively.
This class is taught with Francois-Xavier Coudert, Thiks Stuyver, and Damien Laage. Students get a sense of data in chemistry (classification of data, metadata, data conservation, databases), statistical learning methods, and of deep learning. My own contribution focuses on the basics of deep learning, an introduction to generative modelling, and an illustration through recent applications in chemical and physical sciences.