ERC Consolidator Grant · 2025
Machine-Learning Frameworks for Metal-Ligand Modelling: Applications in Catalysis and Drug Design
Understanding molecular structure, reactivity, and dynamics is essential for advancing catalyst design, drug discovery, and sustainable synthesis. Molecular function arises from the interactions between molecules and their surroundings, which involve a wide range of intermolecular interactions. Among these, metal-ligand interactions stand out due to their tunability. These interactions are key in homogeneous catalysts, supramolecular assemblies, and enzymes. However, accurately modelling them in solution remains challenging, particularly for flexible systems or where solvent effects are relevant. machine learning interatomic potentials (MLIPs) offer a promising avenue to surpass current…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.