In recent years, atomistic machine learning models have become increasingly popular in molecular simulations, given their ability to combine the accuracy of quantum mechanical representations with the speed and efficiency of classical potentials. These models are capable of learning highly complex and multi-dimensional interactions within a local environment but face challenges in capturing long-range behavior. In this work, we apply a formalism to represent long-range electrostatics by decomposition of the Coulombic Ewald energy from the local environment representation. We then explore the role of long-range interactions in machine learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties of water and electrolyte solutions, which highlight the versatility of our models in representing heterogeneous and charged systems. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models fall short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

Handling long-range interactions in atomistic machine learning models of water and electrolyte solutions

ACS Spring Meeting 2021

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