Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant
advancement
in simulation methods, extending the predictive ability of first-principles
methods to previously inaccessible length and time scales. However,
the data-driven nature of these models can lead to difficult-to-detect
errors that can compromise prediction accuracy. To address this challenge,
we introduce a novel fingerprinting approach based on the Chebyshev
Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based
descriptor. Our strategy enables efficient and statistically rigorous
analysis of system configurations used in ML-IAM training and those
generated by their application, e.g., in molecular dynamics simulations.
We demonstrate that these fingerprints can effectively assess novelty
of a configuration relative to an existing data set and determine
dissimilarity among individual configurations, which are two key tasks
in workflows for active learning-based ML-IAM training, data set curation,
and on-the-fly uncertainty quantification.