The Role of Reference Points in Machine-Learned Atomistic Simulation
  Models
Xiangyun Lei Weike Ye Joseph Montoya Tim Mueller Linda Hung Jens Hummelshoej
This paper introduces the Chemical Environment Modeling Theory (CEMT), a
novel, generalized framework designed to overcome the limitations inherent in
traditional atom-centered Machine Learning Force Field (MLFF) models, widely
used in atomistic simulations of chemical systems. CEMT demonstrated enhanced
flexibility and adaptability by allowing reference points to exist anywhere
within the modeled domain and thus, enabling the study of various model
architectures. Utilizing Gaussian Multipole (GMP) featurization functions,
several models with different reference point sets, including finite difference
grid-centered and bond-centered models, were tested to analyze the variance in
capabilities intrinsic to models built on distinct reference points. The
results underscore the potential of non-atom-centered reference points in force
training, revealing variations in prediction accuracy, inference speed and
learning efficiency. Finally, a unique connection between CEMT and real-space
orbital-free finite element Density Functional Theory (FE-DFT) is established,
and the implications include the enhancement of data efficiency and robustness.
It allows the leveraging of spatially-resolved energy densities and charge
densities from FE-DFT calculations, as well as serving as a pivotal step
towards integrating known quantum-mechanical laws into the architecture of ML
models.