An Explainable AI Model for Binary LJ Fluids

TL;DR

This paper presents an AI model that accurately predicts radial distribution functions of binary Lennard-Jones fluids across various conditions, aiding understanding of complex molecular interactions and phase behavior.

Contribution

The study introduces a discretized AI approach for predicting RDFs in binary LJ fluids, demonstrating high accuracy and insights into particle size effects and model limitations.

Findings

AI model predicts RDFs accurately outside training temperature range

Particle size ratio significantly influences microstructure

Model fidelity decreases in regimes with different physics

Abstract

Lennard-Jones (LJ) fluids serve as an important theoretical framework for understanding molecular interactions. Binary LJ fluids, where two distinct species of particles interact based on the LJ potential, exhibit rich phase behavior and provide valuable insights of complex fluid mixtures. Here we report the construction and utility of an artificial intelligence (AI) model for binary LJ fluids, focusing on their effectiveness in predicting radial distribution functions (RDFs) across a range of conditions. The RDFs of a binary mixture with varying compositions and temperatures are collected from molecular dynamics (MD) simulations to establish and validate the AI model. In this AI pipeline, RDFs are discretized in order to reduce the output dimension of the model. This, in turn, improves the efficacy, and reduce the complexity of an AI RDF model. The model is shown to predict RDFs for many unknown mixtures very accurately, especially outside the training temperature range. Our analysis suggests that the particle size ratio has a higher order impact on the microstructure of a binary mixture. We also highlight the areas where the fidelity of the AI model is low when encountering new regimes with different underlying physics.