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Found 2 papers, 1 papers with code
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs).
Uncertainty quantification for predictions of atomistic neural networks
The results clarify that noise and redundancy complicate property prediction for molecules even in cases for which changes - e. g. double bond migration in two otherwise identical molecules - are small.
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