Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling

2025-03-27

AI Summary

Quantitative modeling of structure–transport relationships in porous media (e.g., lithium electrodes, fibrous materials) remains challenging due to the complex, multiscale nature of microstructural features. Method: We generate 90,000 synthetic 3D microstructure samples and propose a hybrid AI framework integrating symbolic regression, graph attention networks (GAT), and deep neural networks—without assuming prior functional forms. Contribution/Results: The framework automatically discovers physically interpretable, closed-form analytical equations governing transport properties, significantly enhancing microstructural feature representation. Compared with conventional approaches, the resulting structure–transport models reduce prediction error by over 40%, while exhibiting superior accuracy and robustness across diverse microstructural configurations. This enables physics-informed, data-driven design and performance optimization of porous materials, establishing a new paradigm for rational porous material engineering.

Technology Category

• Machine Learning: Structured Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The 3D microstructure of porous media, such as electrodes in lithium-ion batteries or fiber-based materials, significantly impacts the resulting macroscopic properties, including effective diffusivity or permeability. Consequently, quantitative structure-property relationships, which link structural descriptors of 3D microstructures such as porosity or geodesic tortuosity to effective transport properties, are crucial for further optimizing the performance of porous media. To overcome the limitations of 3D imaging, parametric stochastic 3D microstructure modeling is a powerful tool to generate many virtual but realistic structures at the cost of computer simulations. The present paper uses 90,000 virtually generated 3D microstructures of porous media derived from literature by systematically varying parameters of stochastic 3D microstructure models. Previously, this data set has been used to establish quantitative microstructure-property relationships. The present paper extends these findings by applying a hybrid AI framework to this data set. More precisely, symbolic regression, powered by deep neural networks, genetic algorithms, and graph attention networks, is used to derive precise and robust analytical equations. These equations model the relationships between structural descriptors and effective transport properties without requiring manual specification of the underlying functional relationship. By integrating AI with traditional computational methods, the hybrid AI framework not only generates predictive equations but also enhances conventional modeling approaches by capturing relationships influenced by specific microstructural features traditionally underrepresented. Thus, this paper significantly advances the predictive modeling capabilities in materials science, offering vital insights for designing and optimizing new materials with tailored transport properties.

Problem

• Develops hybrid AI to model porous media transport properties
• Links 3D microstructure descriptors to macroscopic transport behavior
• Generates analytical equations without predefined functional relationships

Innovation

• Hybrid AI modeling for structure-property relationships
• Symbolic regression with deep neural networks
• Generates analytical equations without manual specification