Spline-based neural network interatomic potentials: blending classical and machine learning models

TL;DR

This paper introduces a spline-based neural network interatomic potential framework that combines classical spline methods with neural networks, offering a computationally efficient, interpretable, and flexible approach for modeling complex atomic datasets.

Contribution

The paper presents the s-NNP framework, blending spline-based classical potentials with neural networks, and demonstrates its interpretability, efficiency, and cross-system applicability.

Findings

Spline filters enable interpretable atomic environment encoding.

s-NNP achieves high accuracy with simplified architecture.

Shared spline filters facilitate cross-system analysis.

Abstract

While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are strictly necessary for constructing high-quality IPs. In this work, we introduce a new MLIP framework which blends the simplicity of spline-based MEAM (s-MEAM) potentials with the flexibility of a neural network (NN) architecture. The proposed framework, which we call the spline-based neural network potential (s-NNP), is a simplified version of the traditional NNP that can be used to describe complex datasets in a computationally efficient manner. We demonstrate how this framework can be used to probe the boundary between classical and ML IPs, highlighting the benefits of key architectural changes. Furthermore, we show that using spline filters for encoding atomic environments results in a readily interpreted embedding layer which can be coupled with modifications to the NN to incorporate expected physical behaviors and improve overall interpretability. Finally, we test the flexibility of the spline filters, observing that they can be shared across multiple chemical systems in order to provide a convenient reference point from which to begin performing cross-system analyses.