标题： 一种可解释的AI模型用于二元LJ流体 显示英文标题

标题： An Explainable AI Model for Binary LJ Fluids

摘要： Lennard-Jones（LJ）流体作为理解分子相互作用的重要理论框架。 二元LJ流体，其中两种不同的粒子种类基于LJ势相互作用，表现出丰富的相行为，并为复杂流体混合物提供了有价值的见解。 在此，我们报告了用于二元LJ流体的人工智能（AI）模型的构建和应用，重点在于其在预测不同条件下的径向分布函数（RDFs）方面的有效性。 从分子动力学（MD）模拟中收集了不同组成和温度的二元混合物的RDFs，以建立和验证AI模型。 在这个AI流程中，RDFs被离散化，以降低模型的输出维度。 这反过来提高了模型的有效性，并降低了AI RDF模型的复杂性。 该模型被证明可以非常准确地预测许多未知混合物的RDFs，尤其是在训练温度范围之外。 我们的分析表明，粒子尺寸比对二元混合物的微观结构有更高的影响。 我们还强调了当遇到具有不同基本物理规律的新领域时，AI模型保真度较低的区域。 显示英文摘要

摘要： 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.