Control of C-H Bond Activation by Mo-Oxo Complexes: pKa or Bond Dissociation Free Energy (BDFE)?

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Revealing a Decisive Role for Secondary Coordination Sphere Nucleophiles on Methane Activation

Density functional theory and ab initio calculations indicate that nucleophiles can significantly reduce enthalpic barriers to methane C-H bond activation. Valence bond analysis suggests the formation of a two-center three-electron bond as the origin for the catalytic nucleophile effect. A predictive model for methane activation catalysis follows, which suggests that strongly electron-attracting and electron-rich radicals, together with both a negatively charged and strongly electron-donating outer sphere nucleophile, result in the lowest reaction barriers. It is corroborated by the sensitivity of the calculated C-H activation barriers to the external nucleophile and to continuum solvent polarity. More generally, from the present studies, one may propose proteins with hydrophobic active sites, available strong nucleophiles, and hydrogen bond donors as attractive targets for engineering novel methane functionalizing enzymes.

Synthesis, characterization, DFT calculations, and reactivity study of a nitrido-bridged dimeric vanadium(iv) complex

Two vanadium(iii) complexes, Cz^tBu(Pyr^iPr)₂VCl₂ (1) and Cz^tBu(Pyr^iPr)₂V(N₃)₂ (2), were synthesized and characterized. Chemical reduction of both 1 and 2 gives the thermally stable nitrido-bridged vanadium(iv) dimer complex, [{Cz^tBu(Pyr^iPr)₂}V]₂(μ-N)₂ (3), which is a rare example of a dimeric vanadium(iv) complex bridged by two nitrido ligands. The nitride ligands of 3 are unreactive due to the well-protected environment provided by the pincer ligand and its substituents, as is supported by its X-ray crystal structure and further described by DFT calculations.

Computational Study of 3d Metals and Their Influence on the Acidity of Methane C-H Bonds

CCSD(T) methods in conjunction with correlation consistent basis sets are used to predict the pK _a for the deprotonation of methane in a 3d metal ion adduct, [M···CH₄]⁺ (M = Sc-Cu), in dimethyl sulfoxide solvent, which is modeled by the SMD continuum solvent model. Results show that the coordination of methane to different M⁺ ions has a substantial difference of ∼27 pK _a units, from most to least acidic, and increases the acidity of the methane C-H bond from ∼8 to 36 pK _a units. Furthermore, even with the omission of the more expensive quadruple and quintuple zeta basis sets in the prediction process, similar trends in pK _a(C-H) as a function of 3d metal ions are exhibited. This research serves to illustrate the substantial effect that metal ion identity has on the acidity of a coordinated hydrocarbon and the utility that correlation consistent basis sets have in lowering the computational cost of modeling larger systems.

Importance of Nitrogen-Hydrogen Bond pKa in the Catalytic Coupling of Alkenes and Amines by Amidate Tantalum Complexes: A Computational Study

Density functional theory (DFT) was carried out to study the impact of substituents with different electronic properties upon hydrogen transfer as the rate-determining step in the hydroaminoalkylation catalytic cycle in order to determine the character of the hydrogen atom in the transition state. In the transition state of the rate-determining step, an N-methylaniline substrate ligates to Ta and transfers its hydrogen to the α-carbon of a five-membered tantallacycle and a Ta-C bond is thus broken. Study of the activation energy barriers resulting from the different para- and meta-substituted N-methylanilines and their correlation with computed pK_a and bond dissociation free energy (BDFE) values of the N-methylanilines show more obvious correlations between pK_a and ΔG^‡ values. Assessing the asynchronicity parameter (η) for the studied substituents reveals that pK_a is a larger driving force in the rate-determining hydrogen transfer reaction than the BDFE, which suggest a reasonable amount of protic character in the transition state, and possible routes to the design of more active catalysts with greater substrate scope.