Spline-based neural network interatomic potentials: blending classical
  and machine learning models
Joshua A. Vita Dallas R. Trinkle
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.