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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.
“Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules”
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“On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks”
Maximilian Seitzer, Arash Tavakoli, Dimitrije Antic and Georg Martius · 2022
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“Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces”
Jonas Busk et al · 2023
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“Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning”
Jesús Carrete et al · 2023
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