Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

Abstract

In machine learning approaches relevant for chemical physics and material science, neural network potentials can be trained on the experimental data. The authors propose a training method applying trajectory reweighting instead of direct backpropagation for improved robustness and reduced computational cost.