CECAM - (Machine) learning how to coarse-grain

The workshop is currently planned to take place as a virtual event! Options to also host this as a partial on-site event are being explored, but will only take place in case the situation permits it. Coarse-grained (CG) models aim at a reduced description of a molecular system, offering not only practical benefits, such as significant computational advantages, but also the means to effectively test what subset of degrees of freedom and interactions are sufficient to describe physical processes of interest [1, 2]. While the last few decades have yielded significant advances in the development of coarse-grained models--from foundational considerations to practical force-field parametrization algorithms and methods--a number of strong assumptions the community makes has plagued its further development. For instance, the persistent description of nonbonded interactions in terms of pairwise functions alone puts a severe bound on the quality of these models, ultimately sacrificing accuracy and transferability.