Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866909556161904640 |
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| author | Hosseinhashemi, Somayeh Rieder, Philipp Furat, Orkun Prifling, Benedikt Wu, Changlin Thon, Christoph Schmidt, Volker Schilde, Carsten |
| author_facet | Hosseinhashemi, Somayeh Rieder, Philipp Furat, Orkun Prifling, Benedikt Wu, Changlin Thon, Christoph Schmidt, Volker Schilde, Carsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling Hosseinhashemi, Somayeh Rieder, Philipp Furat, Orkun Prifling, Benedikt Wu, Changlin Thon, Christoph Schmidt, Volker Schilde, Carsten Materials Science Machine Learning 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. |
| title | Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2503.21560 |