AI 中文总结
研究拥挤介质中多种物理机制对粒子输运的影响，开发物理引导的可解释机器学习框架，结合布朗簇动力学模拟等方法，通过二元胶体系统证明框架有效性，揭示各因素对输运的作用及演变，提供了区分耦合物理机制的通用框架。
AI 中文摘要
拥挤介质中的输运受多种物理机制相互作用支配。定量区分它们的单独和耦合贡献一直是挑战，因为微观结构不断演变。本文开发了一个物理引导的可解释机器学习框架，将布朗簇动力学模拟与替代机器学习模型及基于SHAP的解释相结合，以定量区分总体积分数、探索者分数和模板键灵活性对探索者粒子输运的单独和耦合效应。通过二元胶体系统证明了该框架，结果表明网络形成通过增强瞬态笼蔽使探索者粒子定位，键灵活性通过促进局部键重排成为网络后弛豫的独立调节因子，揭示了拥挤、组成和模板键灵活性的相对贡献如何随受限环境发展而演变。该工作为复杂输运现象中定量区分耦合物理机制提供了通用框架。
英文摘要
Transport in crowded media is governed by the interplay of multiple physical mechanisms. Quantitatively disentangling their individual and coupled contributions remains a longstanding challenge because the evolving microstructure continuously modifies their relative influence. Here, we develop a physics-guided interpretable machine-learning framework that couples Brownian Cluster Dynamics simulations with surrogate machine-learning models and SHAP-based interpretation to quantitatively disentangle the individual and coupled effects of total volume fraction, explorer fraction, and template bond flexibility on explorer-particle transport. We demonstrate the framework using binary colloidal systems in which explorer particles diffuse through a template network formed by irreversible bonds with tunable flexibility. Structural descriptors, mean-squared displacement, intermediate scattering functions, and displacement distributions reveal that network formation localizes explorer particles through enhanced transient caging. Bond flexibility emerges as an independent regulator of post-network relaxation by promoting local bond rearrangements that facilitate the release of transiently caged particles without altering the irreversible network topology. Although crowding and composition dominate the overall transport response, quantitative attribution reveals how the relative contributions of crowding, composition, and template bond flexibility evolve as the confining environment develops. Beyond establishing bond flexibility as a distinct control parameter for relaxation in heterogeneous colloidal networks, this work provides a general physics-guided interpretable machine-learning framework for quantitatively disentangling coupled physical mechanisms in complex transport phenomena.
