Redox Potentials with COSMO-RS: Systematic Benchmarking with Different Databases

First and Second Reductions in an Aprotic Solvent: Comparing Computational and Experimental One-Electron Reduction Potentials for 345 Quinones

Using reference reduction potentials of quinones recently measured relative to the saturated calomel electrode (SCE) in N,N-dimethylformamide (DMF), we benchmark absolute one-electron reduction potentials computed for 345 Q/Q•- and 265 Q•-/Q2- half-reactions using adiabatic electron affinities computed with density functional theory and solvation energies computed with four continuum solvation models: IEF-PCM, C-PCM, COSMO, and SM12. Regression analyses indicate a strong linear correlation between experimental and absolute computed Q/Q•- reduction potentials with Pearson's correlation coefficient (r) between 0.95 and 0.96 and the mean absolute error (MAE) relative to the linear fit between 83.29 and 89.51 mV for different solvation methods when the slope of the regression is constrained to 1. The same analysis for Q•-/Q2- gave a linear regression with r between 0.74 and 0.90 and MAE between 95.87 and 144.53 mV, respectively. The y-intercept values obtained from the linear regressions are in good agreement with the range of absolute reduction potentials reported in the literature for the SCE but reveal several sources of systematic error. The y-intercepts from Q•-/Q2- calculations are lower than those from Q/Q•- by around 320-410 mV for IEF-PCM, C-PCM, and SM12 compared to 210 mV for COSMO. Systematic errors also arise between molecules having different ring sizes (benzoquinones, naphthoquinones, and anthraquinones) and different substituents (titratable vs nontitratable). SCF convergence issues were found to be a source of random error that was slightly reduced by directly optimizing the solute structure in the continuum solvent reaction field. While SM12 MAEs were lower than those of the other solvation models for Q/Q•-, SM12 had larger MAEs for Q•-/Q2- pointing to a larger error when describing multiply charged anions in DMF. Altogether, the results highlight the advantages of, and further need for, testing computational methods using a large experimental data set that is not skewed (e.g., having more titratable than nontitratable substituents on different parent groups or vice versa) to help further distinguish between sources of random and systematic errors in the calculations.

Photoredox matching of earth-abundant photosensitizers with hydrogen evolving catalysts by first-principles predictions

Photoredox properties of several earth-abundant light-harvesting transition metal complexes in combination with cobalt-based proton reduction catalysts have been investigated computationally to assess the fundamental viability of different photocatalytic systems of current experimental interest. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations using several GGA (BP86, BLYP), hybrid-GGA (B3LYP, B3LYP*), hybrid meta-GGA (M06, TPSSh), and range-separated hybrid (ωB97X, CAM-B3LYP) functionals were used to calculate relevant ground and excited state reduction potentials for photosensitizers, catalysts, and sacrificial electron donors. Linear energy correction factors for the DFT/TD-DFT results that provide the best agreement with available experimental reference results were determined in order to provide more accurate predictions. Among the selection of functionals, the B3LYP* and TPSSh sets of correction parameters were determined to give the best redox potentials and excited states energies, ΔEexc, with errors of ∼0.2 eV. Linear corrections for both reduction and oxidation processes significantly improve the predictions for all the redox pairs. In particular, for TPSSh and B3LYP*, the calculated errors decrease by more than 0.5 V against experimental values for catalyst reduction potentials, photosensitizer oxidation potentials, and electron donor oxidation potentials. Energy-corrected TPSSh results were finally used to predict the energetics of complete photocatalytic cycles for the light-driven activation of selected proton reduction cobalt catalysts. These predictions demonstrate the broader usefulness of the adopted approach to systematically predict full photocycle behavior for first-row transition metal photosensitizer-catalyst combinations more broadly.

Unveiling the Borohydride Ion through Force-Field Development

The borohydride ion, BH4-, is an essential reducing agent in many technological processes, yet its full understanding has been elusive, because of at least two significant challenges. One challenge arises from its marginal stability in aqueous solutions outside of basic pH conditions, which considerably limits the experimental thermodynamic data. The other challenge comes from its unique and atypical hydration shell, stemming from the negative excess charge on its hydrogen atoms, which complicates the accurate modeling in classical atomistic simulations. In this study, we combine experimental and computer simulation techniques to devise a classical force field for NaBH4 and deepen our understanding of its characteristics. We report the first measurement of the ion's activity coefficient and extrapolate it to neutral pH conditions. Given the difficulties in directly measuring its solvation free energies, owing to its instability, we resort to quantum chemistry calculations. This combined strategy allows us to derive a set of nonpolarizable force-field parameters for the borohydride ion for classical molecular dynamics simulations. The derived force field simultaneously captures the solvation free energy, the hydration structure, as well as the activity coefficient of NaBH4 salt across a broad concentration range. The obtained insights into the hydration shell of the BH4- ion are crucial for accurately modeling and understanding its interactions with other molecules, ions, materials, and interfaces.

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Comprehensive Mechanistic View of the Hydrolysis of Oxadiazole-Based Inhibitors by Histone Deacetylase 6 (HDAC6)

Histone deacetylase (HDAC) inhibitors used in the clinic typically contain a hydroxamate zinc-binding group (ZBG). However, more recent work has shown that the use of alternative ZBGs, and, in particular, the heterocyclic oxadiazoles, can confer higher isoenzyme selectivity and more favorable ADMET profiles. Herein, we report on the synthesis and biochemical, crystallographic, and computational characterization of a series of oxadiazole-based inhibitors selectively targeting the HDAC6 isoform. Surprisingly, but in line with a very recent finding reported in the literature, a crystal structure of the HDAC6/inhibitor complex revealed that hydrolysis of the oxadiazole ring transforms the parent oxadiazole into an acylhydrazide through a sequence of two hydrolytic steps. An identical cleavage pattern was also observed both in vitro using the purified HDAC6 enzyme as well as in cellular systems. By employing advanced quantum and molecular mechanics (QM/MM) and QM calculations, we elucidated the mechanistic details of the two hydrolytic steps to obtain a comprehensive mechanistic view of the double hydrolysis of the oxadiazole ring. This was achieved by fully characterizing the reaction coordinate, including identification of the structures of all intermediates and transition states, together with calculations of their respective activation (free) energies. In addition, we ruled out several (intuitively) competing pathways. The computed data (ΔG^‡ ≈ 21 kcal·mol^-1 for the rate-determining step of the overall dual hydrolysis) are in very good agreement with the experimentally determined rate constants, which a posteriori supports the proposed reaction mechanism. We also clearly (and quantitatively) explain the role of the -CF₃ or -CHF₂ substituent on the oxadiazole ring, which is a prerequisite for hydrolysis to occur. Overall, our data provide compelling evidence that the oxadiazole warheads can be efficiently transformed within the active sites of target metallohydrolases to afford reaction products possessing distinct selectivity and inhibition profiles.