Uncertainty quantification for predictions of atomistic neural networks

Understand

The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored.

• For this, the architecture of the PhysNet NN was suitably modified and the resulting model was evaluated with different metrics to quantify calibration, quality of predictions, and whether prediction error and the predicted uncertainty can be correlated.
• The results from training on the QM9 database and evaluating data from the test set within and outside the distribution indicate that error and uncertainty are not linearly related.
• The results clarify that noise and redundancy complicate property prediction for molecules even in cases for which changes - e.g.