112 citations · 237 across the 19 of their papers we have counts for
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physics.chem-ph2022★ 1 cited
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Stephan Thaler, Gregor Doehner, Julija Zavadlav
Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied o…
physics.chem-ph2022★ 48 cited
Deep Coarse-grained Potentials via Relative Entropy Minimization
Stephan Thaler, Maximilian Stupp, Julija Zavadlav
Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, p…