7 citations · 13 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2024★ 7 cited
Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials
Klara Bonneau, Jonas Lederer, Clark Templeton +3
Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic r…
q-bio.BM2023★ 6 cited
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…