Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

TL;DR

This paper uses explainable AI to analyze how neural networks learn atomic interactions in molecules, revealing their alignment with chemical principles and implications for stable molecular dynamics.

Contribution

It introduces a framework for analyzing learned atomic interactions in neural network models and compares them with fundamental chemical principles.

Findings

Models that deviate from physical principles lead to unstable molecular dynamics.

Analysis reveals how models predict interactions based on atomic species and distance.

Proposes architectural improvements for better modeling of atomic interactions.

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 molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions.