Prediction rigidities for data-driven chemistry

Abstract

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.

15 pages, 9 figures, submitted to Faraday Discussions: "Data-driven discovery in the chemical sciences", 2024

References in corpus (6)

• Machine-learning based interatomic potential for amorphous carbon
• Nearsightedness of Electronic Matter
• Uncertainty estimation for molecular dynamics and sampling
• Wigner kernels: body-ordered equivariant machine learning without a basis
• Variational Bayesian Last Layers
• A prediction rigidity formalism for low-cost uncertainties in trained neural networks