tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes

Abstract

We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal–organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, −1} e . The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from .

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

“…[32][33][34][35][36][37][38][39] One of the fundamental features underlying much of this work has been the use of high-throughput density functional theory 40 (DFT) workflows to construct large-scale electronic structure-property databases, such as those developed for inorganic solids 29,[41][42][43][44][45][46][47] and molecular systems. [48][49][50][51][52] The synergistic combination of highthroughput DFT databases and ML has led to the discovery of a diverse range of materials with sought-after properties, including efficient organic light-emitting diodes, 53 superhard inorganic materials, 54 and thermally conductive polymers, 55 among many others. 38 With this in mind, there is a significant need for an analogous database of DFT-computed material properties for MOFs so that new ML models can be developed for the rapid prediction of MOF electronic structure properties.…”

Accurate and rapid prediction of pKa of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

“…The performance of GFN2-xTB has also been tested upon a large number of TM complexes taken from the Cambridge structural database. 28,29 However, the performance of GFN2-xTB towards prediction of thermochemical properties of TM complexes has not been extensively validated against experimental and/or DFT computed data.…”

Ab Initio Machine Learning in Chemical Compound Space

“…tmQM 383 contains geometries and common electronic properties (as for QM9) of 86 665 mononuclear complexes extracted from the Cambridge Structural Database (CSD). tmQM includes Werner, bioinorganic and organometallic complexes based on a large variety of organic ligands and 30 transition metals.…”