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Coherent energy and force uncertainty in deep learning force fields
In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions.
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy.
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
In this work, we present the dataset Transition1x containing 9. 6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the wB97x/6-31G(d) level of theory.
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distribution.
