FlexiMOFs-2 · A Design Principle for Predicting Flexible Metal-Organic Frameworks

A Design Principle for Predicting Flexible Metal-Organic Frameworks

Metal-organic frameworks (MOFs) represent one of the most promising classes of porous crystalline materials for energy storage, catalysis, and gas separation. Their modular architecture, based on the assembly of metal nodes and organic linkers, offers vast tunability of their properties and structures to target specific application. However, despite the exponential growth in computationally predicted MOFs, the experimental synthesis of these materials remains a major bottleneck. The main challenge lies in the lack of reproducible and scalable synthetic protocols, which limits the translation of computational predictions into experimentally verified and industrially viable materials. The FlexiMOFs-2 project was conceived to address this challenge by developing predictive, data-driven models that can link the structure and flexibility of MOFs to their synthetic feasibility. The overarching goal was to establish a framework that could accelerate the discovery of new MOFs and provide viable synthesis recommendations based on structure-property-synthesis correlations. Within the broader EU policy framework for digital and sustainable materials innovation, the project aligns closely with the European Green Deal, FAIR data principles, and Horizon Europe’s digital transformation agenda, which promotes transparent, reproducible, and interoperable data infrastructures for chemistry and materials science. During the implementation phase, the project evolved into building a foundational predictive platform, FAIR-MOFs, which systematically bridges the gap between experimental synthesis knowledge and computational MOF data. The resulting database integrates over 45,700 curated crystal structures, 33,361 geometry-optimised structures, and 4,161 synthesis-linked entries, establishing one of the largest FAIR datasets for MOF synthesis. Using this resource, a graph-based neural network model was developed to predict the most probable metal salts, ligands, and solvents directly from 3D MOF structures, thus providing the first structure-informed recommender for synthesis of MOFs. By demonstrating the experimental synthesis of a predicted MOF using model-derived conditions, the project has laid the groundwork for a new paradigm of AI-guided reticular synthesis. Although the initial scope included modelling MOF flexibility the research emphasis naturally converged toward predictive synthesis modelling, which represents a critical precursor to understanding flexibility and adaptive behaviour. The project’s pathway to impact rests on enabling a data-driven synthesis ecosystem that can (i) accelerate the realisation of hypothetical MOFs into tangible materials, (ii) enhance reproducibility and efficiency of MOF synthesis across laboratories, and (iii) support the EU’s vision of open, FAIR, and sustainable materials research. By providing both the database and predictive infrastructure openly to the research community, the outcomes of FlexiMOFs contribute directly to reducing experimental waste, advancing digital chemistry, and fostering interdisciplinary collaboration between computational scientists, synthetic chemists, and data engineers.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз