Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling
Journal:
arXiv
Published Date:
Mar 27, 2025
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.
