PorMatDesign · Machine learning-aided multiscale design of porous materials tailored to application-specific, hydro-mechanical performance requirements

Machine learning-aided multiscale design of porous materials tailored to application-specific, hydro-mechanical performance requirements

PorMatDesign set out to develop a computationally feasible framework for the multi-scale design and optimization of porous materials, whose microscopic structure determines their overall mechanical and hydraulic performance. Such materials are central to technologies ranging from energy storage and filtration to additive manufacturing and biomedical engineering. However, designing them remains a major scientific challenge: the relationships between their pore-level geometry and their large-scale properties are nonlinear, multi-physical, and span several spatial scales. This makes direct optimization computationally prohibitive. The project addressed this problem by integrating machine learning and physics-based simulation into a unified optimization framework. Rather than relying solely on trial-and-error or high-fidelity numerical models, PorMatDesign explored how physics-informed neural networks (PINNs) and advanced optimization strategies could be used to link structure, process, and performance more efficiently. The central objective was to create a tractable simulation–optimization pipeline that can generate porous microstructures tailored to specific target behaviors—such as flow permeability, stiffness, or energy absorption—while maintaining physical realism. This required: - developing mathematical models for reducing the dimensionality of porous-structure representations, - designing multi-scale neural networks that combine information across pore-scale and Darcy-scale physics, - and formulating a topology optimization algorithm capable of searching large design spaces efficiently. The project’s methodological advances were implemented in an open-source software tool called 'Poromotiv', which integrates porous-media generation, descriptor computation, and optimization within a single, reproducible workflow.

Data: CORDIS, © European Union