Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.

ABSTRACT: While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecul

SUBMITTER: Esders M 

PROVIDER: S-EPMC11780731 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature