Interpretable machine-learning enhanced parametrization methodology for Pluronics-water mixtures in DPD simulations.

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Soft Matter Pub Date : 2025-06-19 DOI:10.1039/d5sm00291e
Nunzia Lauriello, Deekshith Naidu Ponnana, Zhan Ma, Karel Šindelka, Antonio Buffo, Gianluca Boccardo, Daniele Marchisio, Wenxiao Pan
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引用次数: 0

Abstract

Dissipative particle dynamics (DPD) is an incredibly powerful tool for simulating the behavior of structured fluids. However, identifying the appropriate model parameters to accurately replicate physical properties remains a challenge. This study showcases the benefits of integrating machine learning techniques into the top-down parameterization of Pluronic systems. The proposed workflow outlines a data-driven approach to accurately determine model parameters tailored to various Pluronic systems. Gaussian process regression (GPR)-based surrogate models effectively replicate the results of DPD simulations, delivering faster responses that streamline parameter optimization and enable the calibration of Pluronic systems against experimental data. Although DPD simulations provide valuable insight, their high computational cost, due to extensive simulations and post-processing, presents a challenge. The GPR-based surrogate model addresses this by modeling the relationships between input parameters and output properties. SHAP (SHapley additive exPlanations) analysis enhances model interpretability, providing deeper insights into the relationships and causal mechanisms between the input parameters and the predicted properties. The combination of GPR and SHAP analysis provides an interpretable machine learning approach, enabling a more efficient optimization process and reducing the need for exhaustive simulations. This work lays a foundation for generalizing the parameterization process across Pluronic systems and conditions, such as varying temperatures, by incorporating additional DPD model input parameters.

DPD模拟中Pluronics-water混合物的可解释机器学习增强参数化方法。
耗散粒子动力学(DPD)是一种非常强大的模拟结构流体行为的工具。然而,确定适当的模型参数以准确地复制物理特性仍然是一个挑战。本研究展示了将机器学习技术集成到Pluronic系统的自顶向下参数化中的好处。提出的工作流程概述了一种数据驱动的方法,以准确确定适合各种Pluronic系统的模型参数。基于高斯过程回归(GPR)的代理模型有效地复制了DPD模拟的结果,提供了更快的响应,简化了参数优化,并能够根据实验数据校准Pluronic系统。虽然DPD模拟提供了有价值的见解,但由于大量的模拟和后处理,它们的高计算成本提出了挑战。基于gpr的代理模型通过建模输入参数和输出属性之间的关系来解决这个问题。SHAP (SHapley加性解释)分析增强了模型的可解释性,为输入参数和预测属性之间的关系和因果机制提供了更深入的见解。GPR和SHAP分析的结合提供了一种可解释的机器学习方法,实现了更有效的优化过程,减少了详尽模拟的需要。这项工作为通过纳入额外的DPD模型输入参数,将参数化过程推广到Pluronic系统和条件(如变温度)奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Soft Matter
Soft Matter 工程技术-材料科学:综合
CiteScore
6.00
自引率
5.90%
发文量
891
审稿时长
1.9 months
期刊介绍: Soft Matter is an international journal published by the Royal Society of Chemistry using Engineering-Materials Science: A Synthesis as its research focus. It publishes original research articles, review articles, and synthesis articles related to this field, reporting the latest discoveries in the relevant theoretical, practical, and applied disciplines in a timely manner, and aims to promote the rapid exchange of scientific information in this subject area. The journal is an open access journal. The journal is an open access journal and has not been placed on the alert list in the last three years.
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