Machine-learned potentials
7 upcoming events
Machine Learning Interatomic Potentials: Bridging Model Development and Interdisciplinary Applications
A CECAM workshop in Taiwan that puts the people who build machine-learned interatomic potentials in the same room as the people who apply them, covering descriptors, message-passing models, transferability and cost.
Studying dynamics in soft matter and porous materials
CECAM Flagship School on dynamics in soft matter and porous materials, covering polymer dynamics, lattice Boltzmann, coarse-graining, and machine learning, with hands-on sessions using ESPResSo and waLBerla.
Computational electrochemistry in the era of AI
Four days on simulating electrochemical interfaces for batteries, fuel cells and electrolysers, and on what machine-learned potentials and AI-driven workflows change about how those interfaces are modelled.
Automating atomistic machine learning
Machine-learned interatomic potentials still lean on hand-tuning and personal experience. This workshop looks at the automated workflows, provenance and reproducible benchmarking needed to make them a routine tool.
Machine Learning Assisted Molecular (Thermo)Dynamics
CECAM Flagship workshop on machine learning for molecular simulations, discussing ML interatomic potentials, coarse-grained models, generative models, and challenges in transferability, reliability, and scalability.
Challenges in ionic fluids: Theory, simulation, and experiment
Workshop on ionic fluids, uniting theory, simulation, and experiment to tackle multiscale challenges. Topics include ML potentials, classical DFT, and advanced spectroscopic techniques.
Current Topics in Theoretical Chemistry 2027
Workshop on current topics in theoretical chemistry, focusing on density functional theories and dynamics simulations, including nonadiabatic nuclear-electronic dynamics and machine learning for catalysis.