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Found 6 papers, 5 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.
Collective Variables for the Study of Crystallization
Enhanced sampling methods can overcome this time scale hurdle.
Soft Condensed Matter
A Unified Approach to Enhanced Sampling
The resulting method is very general and can be used to achieve different types of enhanced sampling.
Computational Physics
Rethinking Metadynamics
Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is function of a selected number of collective variables.
Computational Physics
Simulations of Crystal Nucleation from Solution at Constant Chemical Potential
A widely spread method of crystal preparation is to precipitate it from a supersaturated solution.
Soft Condensed Matter Chemical Physics
