Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes

Abstract

We survey more than 240 000 crystallized mononuclear transition metal complexes (TMCs) to identify trends in preferred geometric structure and metal coordination. While we observe that an increased level of d filling correlates with a lower coordination number preference, we note exceptions, and we observe undersampling of 4d/5d transition metals and 3p-coordinating ligands. For the one-third of mononuclear TMCs that are octahedral, analysis of the 67 symmetry classes of their ligand environments reveals that complexes often contain monodentate ligands that may be removable, forming an open site amenable to catalysis. Due to their use in catalysis, we analyze trends in coordination by tetradentate ligands in terms of the capacity to support multiple metals and the variability of coordination geometry. We identify promising tetradentate ligands that co-occur in crystallized complexes with labile monodentate ligands that would lead to reactive sites. Literature mining suggests that these ligands are untapped as catalysts, motivating proposal of a promising octa-functionalized porphyrin.

Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

“…Analysis by Kulik based on > 240 000 mononuclear complexes in the Cambridge Structural Database (CSD) showed that approximately one third of them are octahedral and often contain monodentate ligands which can dissociate to leave empty coordination sites for catalysis. [47] Thus, the authors proceeded with a design of square-planar tetradentate ligands, leaving two coordination sites for labile monodentate ligands. This went against conventional mechanistic understanding of metal catalysed coupling reactions.…”

MACE: Automated Assessment of Stereochemistry of Transition Metal Complexes and Its Applications in Computational Catalysis

“…Computational exploration of transition metal chemical space is, or at least is expected to be, a key part of the rational development of new catalysts − and advanced functional materials with promising optical, sensing, and magnetic properties. − Progress in development of computational and data-driven tools for transition metal complexes (TMCs) has considerably facilitated such research. − Special note must be made of the recently introduced xTB software supporting molecular mechanics and semiempirical methods, which provide robust and powerful toolsets for quick geometry preoptimization of TMCs, and fast highly accurate post-Hartree–Fock methods such as DPLNO-CCSD­(T), allowing the complex electronic structure problems of the TMC chemistry that are inaccessible to conventional DFT calculations to be rationalized. , The transition toward automated in silico TMC exploration and analysis has been greatly facilitated by the introduction of reliable three-dimensional (3D) structure generation tools, which partially or completely automate the construction of starting molecular models. − …”

Modeling Fe(II) Complexes Using Neural Networks

“…■ METHOD Data Set. Nandy and co-workers 43 reported a comprehensive data set which includes more than 240,000 crystallized mononuclear transition metal complexes (TMCs) from The Cambridge Structural Database (CSD). 44 We followed their procedure 30 to curate "computation-ready" complexes, i.e., both oxidation states and charges are already specified upon uploading without hydrogen atoms missing in the structures.…”