The Role of Reference Points in Machine-Learned Atomistic Simulation Models

• URL: http://arxiv.org/abs/2310.18552v1
• Date: Sat, 28 Oct 2023 01:02:14 GMT
• Title: The Role of Reference Points in Machine-Learned Atomistic Simulation Models
• Authors: Xiangyun Lei, Weike Ye, Joseph Montoya, Tim Mueller, Linda Hung, Jens Hummelshoej
• Abstract summary: Chemical Environment Modeling Theory (CEMT) is designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models. It allows the leveraging of spatially-resolved energy densities and charge densities from FE-DFT calculations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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