Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Friederich, Pascal; Gomes, Gabriel; Bin, Riccardo De; Aspuru‐Guzik, Alán; Balcells, David

doi:10.1039/d0sc00445f

Cited by 141 publications

(180 citation statements)

References 128 publications

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“…Machine learning is a powerful tool for chemists 6,7 to identify patterns in complex datasets from composite libraries or high-throughput experimentation. 8 Chemical challenges including retrosynthesis, 9 reaction performance 10 and products, 11,12 the identification of new materials and catalysts, 13,14,15 as well as enantioselectivity 16,17 have been addressed. However, a significant challenge is predictability of reactions involving S N 1 or S N 1-type mechanisms 18 in the absence of chiral catalysts/ligands, 19 due to the potentially unclear mechanistic pathways resulting from the instability of the carbocationic intermediate.…”

Section: Introductionmentioning

confidence: 99%

Predicting glycosylation stereoselectivity using machine learning

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Predicting glycosylation stereoselectivity using machine learning

et al. 2021

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“…[9][10][11] Neural networks [12][13][14][15][16] and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17-21 and drug discovery. [22][23][24][25][26] ML and data-driven approaches are also making a rapid progress in catalytic, [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] organic, [42][43][44][45][46][47] inorganic 48,49 and theoretical [50][51][52][53][54][55][56] chemistry.…”

Section: Introductionmentioning

confidence: 99%

“… 9 − 11 Neural networks 12 − 16 and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17 − 21 and drug discovery. 22 − 26 ML and data-driven approaches are also making rapid progress in catalytic, 27 − 41 organic, 42 − 47 inorganic, 48 , 49 and theoretical 50 − 56 chemistry.…”

Section: Introductionmentioning

confidence: 99%

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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“…[38][39][40] Similarly, high-throughput simulation approaches that have been typically aimed at static properties of single organic molecules [41][42][43][44] are increasingly targeting reactivity. [45][46][47][48][49] Here, we demonstrate the combined application of high-throughput (HT) simulation and ML interatomic potentials to study a complex reactive system dominated by dynamical effects. Specifically, we studied the pericyclic reactions involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene.…”

Section: Introductionmentioning

confidence: 99%

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

Wang²,

Schwalbe-Koda³

et al. 2020

Preprint

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Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires ab initio molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a training dataset size of approximately 31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. These tools are then used to automatically predict a reaction mechanism that is in agreement with the experimentally-reported product distribution. In addition, thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a very minor role in the reaction. Furthermore, a transfer-learning strategy effectively allowed to upgrade the potential energy surface to higher levels of theory (def2-TZVPD basis set and double hybrid functional) using less than 10% additional calculations. Since these methods capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for entropic intermediates. This overall approach is broadly applicable and opens the door to the study of dynamical effects in larger, previously-intractable reactive systems.

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Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Abstract: A machine learning exploration of the chemical space surrounding Vaska's complex.

Cited by 141 publications

References 128 publications

Predicting glycosylation stereoselectivity using machine learning

Predicting glycosylation stereoselectivity using machine learning

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

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

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