HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations

arXiv:2404.16911v1 Announce Type: cross
Abstract: Molecular simulations have assumed a paramount role in the fields of chemistry, biology, and material sciences, being able to capture the intricate dynamic properties of systems. Within this realm, coarse-grained (CG) techniques have emerged as invaluable tools to sample large-scale systems and reach extended timescales by simplifying system representation. However, CG approaches come with a trade-off: they sacrifice atomistic details that might hold significant relevance in deciphering the investigated process. Therefore, a recommended approach is …

abstract arxiv atom biology chemistry cs.lg dynamic fields graph graph neural network material network neural network physics.chem-ph q-bio.bm realm role sample scale simulations systems tools type universal