Building the Data Foundations for the Design and Discovery of Transition Metal Complexes

The unique chemistry of transition metal complexes (TMCs) enables several advanced technologies in diverse fields including renewable energies, medicine, and catalysis. Machine learning methods can accelerate their rational design but are bounded by limited dataset sizes, restricted chemical scopes, and insufficient quality. This Perspective surveys the current landscape of TMC datasets and investigates how they seek to address these challenges. The analysis focuses on the data curation processes as well as the scope of the properties provided, and their level of theory. Furthermore, the applications and methodological advancements enabled by TMC datasets are reviewed, including the generative design of metal ligands and the evolutionary optimization of TMCs. The Perspective concludes by outlining future directions, with particular emphasis on extending data to broader chemical scopes including non-equilibrium geometries, establishing community-wide data formats and benchmarks, and developing strategies to effectively integrate heterogeneous data sources.