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Found 3 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).
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 sampling of molecular geometries with uncertainty-based adversarial attacks
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data.
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