7 citations · 19 across the 5 of their papers we have counts for
5 papers
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…
Accurate nuclear quantum statistics on machine-learned classical effective potentials
Iryna Zaporozhets, Félix Musil, Venkat Kapil +1
The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development…
Learning data efficient coarse-grained molecular dynamics from forces and noise
Aleksander E. P. Durumeric, Yaoyi Chen, Frank Noé +1
Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from referenc…
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…
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Andreas Krämer, Aleksander P. Durumeric, Nicholas E. Charron +3
Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training…