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Found 4 papers, 2 papers with code
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP).
Learning Collective Variables with Synthetic Data Augmentation through Physics-inspired Geodesic Interpolation
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs.
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning.
Differentiable Simulations for Enhanced Sampling of Rare Events
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs).
