Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials

This review presents the key aspects and development directions of materials informatics, emphasizing the role of artificial intelligence (AI) and machine learning (ML) in materials science research. The objective is to provide a comprehensive overview of materials informatics tools, workflows, and case studies, particularly aimed at experimental researchers unfamiliar with AI frameworks. Basic concepts are introduced and traditional modelling methods compared to AI/ML-assisted models. Existing material models serve as a foundation for advanced modelling and simulations aimed at reducing the time required for characterisation and discovery, with physics-based models gaining importance in the development of AI-supported surrogate models. This review also covers currently available resources, including: (i) software for solving complex mathematical equations and material modelling; (ii) web-based platforms and tools designed for both expert and non-expert users; and (iii) materials data repositories, prioritising standardisation. Case examples involving materials with architectured macro-, micro-, and nano-porosity are reviewed across three material types: metal-organic frameworks (MOFs), electrospun PVDF piezoelectrics, and 3D printed mechanical metamaterials. Traditional computational models offer interpretability and physical consistency, AI/ML excels in speed and complexity handling but may lack transparency. Hybrid models combining both approaches show excellent results in prediction, simulation, and optimisation, offering both speed and interpretability. Progress depends on modular, interoperable AI systems, standardised FAIR data, and cross-disciplinary collaboration. Addressing data quality and integration challenges will resolve issues related to metadata gaps, semantic ontologies, and data infrastructures, especially for small datasets and unlock transformative advances in fields like nanocomposites, MOFs, and adaptive materials.