Structure Optimisation of Large Transition‐Metal Complexes with Extended Tight‐Binding Methods

Bursch, Markus; Neugebauer, Hagen; Grimme, Stefan

doi:10.1002/ange.201904021

Cited by 31 publications

(37 citation statements)

References 68 publications

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“…The GFN2 parametrization is less empirical than the GFN1 and it was proven to be more robust in geometry optimization. 84 The tight optimization level was used in the GFN2-xTB calculations to set the convergence thresholds to 1•10 −6 E h (energy) and 8•10 −4 E h α −1 (gradient). The calculations were carried out with the xtb program.…”

Section: Quantum Geometries and Propertiesmentioning

confidence: 99%

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

Balcells

Skjelstad

2020

J. Chem. Inf. Model.

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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 .

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Section: Quantum Geometries and Propertiesmentioning

confidence: 99%

“…The 7% geometries yielding the largest S q values were excluded. 84 3. Electron-count filter: Excluded all structures with an odd number of electrons.…”

Section: Quantum Geometries and Propertiesmentioning

confidence: 99%

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

Balcells

Skjelstad

2020

J. Chem. Inf. Model.

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“…In future practical applications, machine-learning J only makes sense if the benefit of replacing a conventional approach by ML is not marginal compared to the overall cost of obtaining molecular structures. Besides the implicit learning of molecular structures mentioned above, efficient ways of obtaining them could be the extraction from X-ray crystallographic or computational databases, or structural optimizations via cheap methods such as force-fields or tight-binding approaches [122][123][124][125][126] , or explicitly via ML models 36,127 .…”

Section: Data Set Construction and First-principles Calculationsmentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

et al. 2020

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Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. File list (4) download file view on ChemRxiv supp.pdf (2.07 MiB) download file view on ChemRxiv ms.pdf (5.17 MiB) download file view on ChemRxiv Cu2-dataset.zip (22.06 MiB) download file view on ChemRxiv py_regression.zip (17.79 MiB)

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“…The xTB methods are tight-binding quantum chemical methods for the geometry optimisation of systems containing elements up to Z = 82, and represent a robust and significantly cheaper alternative to DFT for metalcontaining species. 60 A comparison of the xTB (specifically GFN2-xTB) and DFT-calculated energies of previously reported systems was undertaken (structures were taken directly from the computational workflow described below). DFT energies were obtained from single-point energy evaluations of xTB geometries using similar methods to those recently applied to related systems 25 (PBE0 61 level of theory using the Ahlrichs basis set def2-SVP, 62, 63 Grimme's D3BJ dispersion correction 64 and the polarizable continuum model (PCM) 65 implicit solvation representing DMSO; more details are available in Supporting Information Section S3).…”

Section: Semi-empirical Methods For Cage Relative Energy Evaluationmentioning

confidence: 99%

“…stko provides methods to optimise metal-containing systems including the UFF4MOF forcefield 66,67 in GULP (version 5.1), 69,70 and the xtb software. 59,60 See Supporting Information Section S2 for further details. After construction with stk, the lowest energy cage conformer was found using the following sequence of optimisation steps:…”

Section: Ligand and Cage Assemblymentioning

confidence: 99%

High-throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design

Tarzia¹,

Lewis

Jelfs³

2021

Preprint

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The use of unsymmetrical components in metallo-supramolecular chemistry allows for low-symmetry architectures with anisotropic cavities toward guest-binding with high specificity and affinity. Unsymmetrical ditopic ligands mixed with Pd(II) have the potential to self-assemble into reduced symmetry Pd2L4 metallo-architectures. Mixtures of isomers can form, however, resulting in potentially undesirable heterogeneity within a system. Therefore it is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into a single isomer under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our low-cost computational workflow rapidly constructs new unsymmetrical ligands (and Pd2L4 cage isomers) and ranks their likelihood for forming cis-Pd2L4 assemblies. From this narrowed search space, we successfully synthesised four new low-symmetry, cis-Pd2L4 cages, with cavities of different shapes and sizes.

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Structure Optimisation of Large Transition‐Metal Complexes with Extended Tight‐Binding Methods

Cited by 31 publications

References 68 publications

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

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

Exchange Spin Coupling from Gaussian Process Regression

High-throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design

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