As a result of a high degree of internal collaboration, each research group at the IMDEA Materials Institute participates in several of our research programmes. Driven by the talent of the researchers, the research programmes combine cutting-edge fundamental oriented research in topics at the frontiers of knowledge with applied research encompassing the midterm interest of our industrial partners to provide long-term technological leadership.
The Novel Materials programme combines expertise in the design and synthesis of nano-, molecular-, and bio-derived building blocks with their integration into macroscopic materials, devices, and complex 3D architectures. It develops solutions for high-performance structural components with enhanced multifunctional capabilities such as thermal, electrical, fire-resistant, mechanical, and biological performance, as well as functional nanostructured materials for sustainable energy-related catalytic applications. Materials and devices developed in this programme are used in transport, energy, and biomedical sectors.
The Advanced Manufacturing programme covers a broad range of materials (e.g. metallic alloys, biomaterials, polymers, composites) and combines experimental, computational, and data-driven efforts. It aims to improve the quality, productivity, efficiency and sustainability of current manufacturing methods and to develop novel and hybrid manufacturing techniques, e.g. additive manufacturing (AM), to enable the commercial realisation of emerging products in aerospace, biomedical, energy, automotive and other sectors.
The Integrated Computational Materials Engineering programme integrates simulation tools into multiscale modeling strategies to design, model and optimise materials, metamaterials, and their processing routes before laboratory manufacturing. It links processing, microstructure, properties and performance across electronic, atomistic, mesoscopic and continuum scales, using in-house solvers (e.g. crystal plasticity, phase-field, finite elements), high-performance computing, machine learning, surrogate modeling and advanced calibration to improve efficiency, fidelity and predictive capability.
The Multiscale and In Situ Characterisation programme focuses on understanding microstructure/defect evolution in advanced materials during processing and service using advanced characterisation techniques, such as 3D characterisation of materials, including microstructural, chemical, catalytic and crystallographic information across several scales and in-situ/operando characterisation of processes across multiple scales (4D characterisation).
The Digitalisation and Artificial Intelligence programme coordinates the development of AI- and data-driven methodologies for materials science and engineering, while supporting the associated digitalisation infrastructure, including workflows, databases, and ontologies. Efforts focus on accelerating materials discovery via machine learning, enabling self-driving laboratories for advancing materials design and manufacturing through digital twins, dynamic experimental feedback loops, FAIR data frameworks, uncertainty quantification and parameter optimisation.