Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery.@en

Homogeneous ruthenium catalysed methanol dehydrogenation could become a key reaction for hydrogen production in liquid fuel cells. In order to improve existing catalytic systems, mechanistic insight is paramount in directing future studies. Herein, we describe what computational mechanistic research has taught us so far about ruthenium catalysed dehydrogenation reactions. In general, two mechanistic pathways can be operative in these reactions: a metal-centered or a metal-ligand cooperative (Noyori-Morris type) minimum energy reaction pathway (MERP). Discerning between these mechanisms on the basis of computational studies has proven to be highly input dependent, and to circumvent pitfalls it is important to consider several factors, such as solvent effects, metal-ligand cooperativity, alternative geometries, and complex electronic structures of metal centres. This Frontiers article summarizes the reported computational research performed on ruthenium catalyzed dehydrogenation reactions performed in the past decade, and serves as a guide for future research.

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The electrocatalytic CO2 reduction reaction (CO2RR) is one of the key technologies of the clean energy economy. Molecular-level understanding of the CO2RR process is instrumental for the better design of electrodes operable at low overpotentials with high current density. The catalytic mechanism underlying the turnover and selectivity of the CO2RR is modulated by the nature of the electrocatalyst, as well as the electrolyte liquid, and its ionic components that form the electrical double layer (EDL). Herein we demonstrate the critical non-innocent role of the EDL for the activation and conversion of CO2 at a high cathodic bias for electrocatalytic conversion over a silver surface as a representative low-cost model cathode. By using a multiscale modeling approach we demonstrate that under such conditions a dense EDL is formed, which hinders the diffusion of CO2 towards the Ag111 electrocatalyst surface. By combining DFT calculations and ab initio molecular dynamics simulations we identify favorable pathways for CO2 reduction directly over the EDL without the need for adsorption to the catalyst surface. The dense EDL promotes homogeneous phase reduction of CO2via electron transfer from the surface to the electrolyte. Such an outer-sphere mechanism favors the formation of formate as the CO2RR product. The formate can undergo dehydration to CO via a transition state stabilized by solvated alkali cations in the EDL.@en

Activation of homogeneous catalysts is an important step in ensuring efficient operation of any catalytic system as a whole. For the majority of pincer catalysts, the activation step leans heavily on the metal ligand cooperative chemistry that allows these complexes to react with small molecule substrates and engage in catalytic transformations. While the majority of such catalysts require a single activation event to become cooperative, herein we report an exception to this trend. Specifically, we demonstrate that a Ru-PN³P aminopyridine pincer catalyst, which lacks conventional reactivity with hydrogen upon typical one-fold activation, can exhibit this reactivity when a sequential two-step activation is performed. The resulting anionic complexes readily activate molecular hydrogen and react further with CO₂ showing the previously unknown reactivity that is critical for CO₂ hydrogenation catalysts. While active in CO₂ hydrogenation, Ru-PN³Ps are significantly more efficient in hydrogenation of bicarbonates - a likely consequence of the chemistry of these pincers requiring formation of anionic complexes for hydrogen activation.

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The choice of a solvent and the reaction conditions often defines the overall behavior of a homogeneous catalytic system by affecting the preferred reaction mechanism and thus the activity and selectivity of the catalytic process. Here, we explore the role of solvation in the mechanism of ketone reduction using a model representative of a bifunctional Mn-diamine catalyst through density functional theory calculations in a microsolvated environment by considering explicit solvent and fully solvated ab initio molecular dynamics simulations for the key elementary steps. Our computational analysis reveals the possibility of a Meerwein-Ponndorf-Verley (MPV) type mechanism in this system, which does not involve the participation of the N-H moiety and the formation of a transition-metal hydride species in ketone conversion. This path was not previously considered for Mn-based metal-ligand cooperative transfer hydrogenation homogeneous catalysis. The MPV mechanism is strongly facilitated by the solvent molecules present in the reaction environment and can potentially contribute to the catalytic performance of other related catalyst systems. Calculations indicate that, despite proceeding effectively in the second coordination sphere of the transition-metal center, the MPV reaction path retains the enantioselectivity preference induced by the presence of the small chiral N,N′-dimethyl-1,2-cyclohexanediamine ligand within the catalytic Mn(I) complex.

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Finding alternative ways to tailor the electronic properties of a catalyst to actively and selectively drive reactions of interest has been a growing research topic in the field of electrochemistry. In this Letter, we investigate the tuning of the surface electronic properties of electrocatalysts via polymer modification. We show that when a nickel oxide water oxidation catalyst is coated with polytetrafluoroethylene, stable Ni-CFx bonds are introduced at the nickel oxide/polymer interface, resulting in shifting of the reaction selectivity away from the oxygen evolution reaction and toward hydrogen peroxide formation. It is shown that the electron-withdrawing character of the surface fluorocarbon molecule leaves a slight positive charge on the water oxidation intermediates at the adjacent active nickel sites, making their bonds weaker. The concept of modifying the surface electronic properties of an electrocatalyst via stable polymer modification offers an additional route to tune multipathway reactions in polymer/electrocatalyst environments, like with ionomer-modified catalysts or with membrane electrode assemblies.

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Rapid and accurate prediction of reactivity descriptors of transition metal (TM) complexes is a major challenge for contemporary quantum chemistry. The recently-developed GFN2-xTB method based on the density functional tight-binding theory (DFT-B) is suitable for high-throughput calculation of geometries and thermochemistry for TM complexes albeit with moderate accuracy. Herein we present a data-augmented approach to improve substantially the accuracy of the GFN2-xTB method for the prediction of thermochemical properties using pK_avalues of TM hydrides as a representative model example. We constructed a comprehensive database forca.200 TM hydride complexes featuring the experimentally measured pK_avalues as well as the GFN2-xTB-optimized geometries and various computed electronic and energetic descriptors. The GFN2-xTB results were further refined and validated by DFT calculations with the hybrid PBE0 functional. Our results show that although the GFN2-xTB performs well in most cases, it fails to adequately describe TM complexes featuring multicarbonyl and multihydride ligand environments. The dataset was analyzed with the ordinary least squares (OLS) fitting and was used to construct an automated machine learning (AutoML) approach for the rapid estimation of pK_aof TM hydride complexes. The results obtained show a high predictive power of the very fast AutoML model (RMSE ∼ 2.7) comparable to that of the much slower DFT calculations (RMSE ∼ 3). The presented data-augmented quantum chemistry-based approach is promising for high-throughput computational screening workflows of homogeneous TM-based catalysts.

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Reversible dissociation of H−X bond (M−L+H−X→M(X)-L(H); (Formula presented.)) is an important step during pre-activation, catalysis and possible deactivation of acid-base cooperative pincer based transition metal catalysts (M−L). Herein we carried out a high-throughput computational investigation of the thermodynamic stability of different adducts in various functionalized Mn(I) based pincer complexes. We used a combination of density functional theory (DFT) and density functional tight binding (DFTB) calculations to analyze (Formula presented.) of >700 (M(X)-L(H)) intermediates based on functionalized variants of four pincer type ligand scaffolds derived from PCP, CNC, PNP and SNS ligands. We discovered linear scaling relations between (Formula presented.) of various species. Strongest correlations were found between species of similar size and chemical nature e. g. (Formula presented.) correlated best with (Formula presented.) and worst with (Formula presented.). Such scaling relations can be useful for property based screening of catalysts and selection of (co)solvent/substrate/base for optimized reaction conditions. We also investigated the influence of the ligand backbone and the functionalization of donor and backbone sites in the ligand. Our analysis reveals the crucial role of the second coordination sphere functionalization for the reactivity of the complexes with impact in some cases exceeding that of the variation of the functional groups directly attached to the donor atoms.

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In the quest for active and inexpensive (photo)electrocatalysts, atomistic simulations of the oxygen evolution reaction (OER) are essential for understanding the catalytic process of water splitting at solid surfaces. In this paper, the enhancement of the OER by first-row transition-metal (TM) doping of the abundant semiconductor ZnO was studied using density functional theory (DFT) calculations on a substantial number of possible structures and bonding geometries. The calculated overpotential for undoped ZnO was 1.0 V. For TM dopants in the 3d series from Mn to Ni, the overpotentials decreased from 0.9 V for Mn and 0.6 V for Fe down to 0.4 V for Co, and rose again to 0.5 V for Ni and 0.8 V for Cu. The overpotentials were analyzed in terms of the binding to the surface of the species involved in the four reaction steps of the OER. The Gibbs free energies associated with the adsorption of these intermediate species increased in the series from Mn to Zn, but the difference between OH and OOH adsorption (the species involved in the first, respectively the third reaction step) was always in the range 3.0–3.3 eV, despite a considerable variation in possible bonding geometries. The bonding of the O intermediate species (involved in the second reaction step), which is optimal for Co, and to a somewhat lesser extend for Ni, then ultimately determined the overpotential. These results implied that both Co and Ni are promising dopants for increasing the activity of ZnO-based anodes for the OER.

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Photoelectrochemical (PEC) splitting of water to make hydrogen is a promising clean-energy technology. The oxygen evolution reaction (OER) largely determines the energy efficiency in PEC water-splitting. Hematite, which is a cheap and sustainable semiconductor material with excellent chemical properties, a favourable band gap (2.1 eV) and composed of earth abundant elements is a suitable model photoanode material for studying OER. To understand the design of energy efficient anodes, it is highly desirable to have mechanistic insight into OER at an atomistic level which can be directly connected to experimentally measured quantities. We present a multiscale computational model of OER which connects the thermodynamics and kinetics of elementary charge transfer reactions in OER to kinetics of OER at laboratory length and time scales. We couple density functional theory (DFT) and DFT based molecular dynamics (DFT-MD) simulations with solvent effects at an atomistic level with kinetic Monte Carlo (kMC) simulations at a coarse-grained level in our multiscale model. The time and applied bias potential dependent surface coverage, which are experimentally not known, and the O2 evolution rate during OER at the hematite-water interface are calculated by the multiscale model. Furthermore, the multiscale model demonstrates the effect of explicitly modelling the interaction of water with the electrode surface via direct adsorption. This journal is

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