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Found 3 papers, 2 papers with code
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations.
Characterizing metastable states with the help of machine learning
Present-day atomistic simulations generate long trajectories of ever more complex systems.
Training collective variables for enhanced sampling via neural networks based discriminant analysis
A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables.
Computational Physics Chemical Physics
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