Bioinspired Design and Computational Prediction of Iron Complexes with Pendant Amines for the Production of Methanol from CO2 and H2

Homogeneous Catalytic CO2 Hydrogenation by [Fe]-Hydrogenase Bioinspired Complexes: A Computational Study

Computational modeling at the DLPNO-CCSD(T)/CBS//M06-L/def2-TZVP level of theory was used to propose four different iron catalysts whose structures were inspired on the [Fe]-hydrogenase active site: [Fe(^MeP^NNH^NP)(acmp)] (C(1), ^MeP^NNH^NP = 2,6-bis(dimethylphosphine), acmp = acylmethylpyridine), [Fe(C^NNH^NC)(acmp)] (C(2), C^NNH^NC = 2,6-bis(methylimidazol-2-ylidene)), [Fe(^MeP^NN^NP)(acmp)] (D(1), ^MeP^NN^NP = 2,6-bis((dimethylphosphine)pyridine)), and [Fe(C^NN^NC)(acmp)] (D(2), C^NN^NC = 2,6-bis((methylimidazol-2-ylidene) pyridine)). Through these electronic structure calculations, the catalytic mechanism of the reaction was explored. The intermediates and transition states present along the reaction coordinate were identified and described as to their equilibrium geometries, vibrational frequencies, and energies. Quasi-harmonic corrections were performed considering conditions analogous to those used experimentally. To compare the catalytic activities of the studied catalysts, turnover frequencies (TOFs) were calculated. Based on the explored catalytic cycles and TOF values (D(1) > C(1) > D(2) > C(2)), the most suitable iron catalysts are those with tridentate phosphine pincer-type ligands coordinated to the metal center. These systems are new promising iron catalysts to promote the CO₂ hydrogenation to formic acid without any use of bases or additives.

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.

Bioinspired Design and Computational Prediction of SCS Nickel Pincer Complexes for Hydrogenation of Carbon Dioxide

Inspired by the structures of the active site of lactate racemase and H2 activation mechanism of mono-iron hydrogenase, we proposed a series of sulphur–carbon–sulphur (SCS) nickel complexes and computationally predicted their potentials for catalytic hydrogenation of CO2. Density functional theory calculations reveal a metal–ligand cooperated mechanism with the participation of a sulfur atom in the SCS pincer ligand as a proton receiver for the heterolytic cleavage of H2. For all newly proposed complexes containing functional groups with different electron-donating and withdrawing abilities in the SCS ligand, the predicted free energy barriers for the hydrogenation of CO2 to formic acid are in a range of 22.2–25.5 kcal/mol in water. Such a small difference in energy barriers indicates limited contributions of those functional groups to the charge density of the metal center. We further explored the catalytic mechanism of the simplest model complex for hydrogenation of formic acid to formaldehyde and obtained a total free energy barrier of 34.6 kcal/mol for the hydrogenation of CO2 to methanol.

Biomimetic heterobimetallic architecture of Ni(ii) and Fe(ii) for CO2 hydrogenation in aqueous media. A DFT study

In this work, density functional theory has been employed to design a heterobimetallic catalyst of Ni(ii) and Fe(ii) for the effective CO2 hydrogenation to HCOOH. Based on computational results, our newly designed catalyst is found to be effective for such conversion reactions with free energy as low as 14.13 kcal mol-1 for the rate determining step. Such a low value of free energy indicates that the NiFe heterobimetallic catalyst can prove to be very efficient for the above said conversion. Moreover, the effects of ligand substitutions at the active metal center and the effects due to various spin states are also explored, and can serve as a great tool for the rational design of NiFe catalyst for CO2 hydrogenation.

Bio-mimetic self-assembled computationally designed catalysts of Mo and W for hydrogenation of CO2/dehydrogenation of HCOOH inspired by the active site of formate dehydrogenase

Density functional theory modelling has been used to design Mo and W-based catalysts MoIII(tBu)(CO) and WIII(tBu)(CO) for CO2 hydrogenation and HCOOH dehydrogenation, which are bio-mimics of the active site of formate dehydrogenase. Based on DFT calculations, the molybdenum and tungsten based complexes are good catalysts in the +3 oxidation state for CO2 hydrogenation with free energies of 24.03 and 21.31 kcal mol-1, respectively. Such a low barrier indicates that our newly designed Mo and W-based complexes are very efficient for CO2 hydrogenation or HCOOH dehydrogenation catalysis. Overall, our computational results provide in depth insights that can serve as a great tool for the design and development of new and efficient molybdenum and tungsten based catalysts for CO2 hydrogenation or HCOOH dehydrogenation.

Computational prediction of pentadentate iron and cobalt complexes as a mimic of mono-iron hydrogenase for the hydrogenation of carbon dioxide to methanol

A series of amidate-ligated pentadentate iron and cobalt complexes with N-heterocyclic pyridinol groups were proposed and computationally screened as potential catalysts for CO₂ reduction. Density functional theory calculations reveal a ligand assisted heterolytic H₂ cleavage mechanism with a total free energy barrier of 23.3 kcal mol^-1 for the hydrogenation of CO₂ to methanol catalysed by a pentadentate Co complex with a 2-[bis(pyridine-2-ylmethyl)]amino-N-3,9-purin-2-one ligand.

A mechanistic study and computational prediction of iron, cobalt and manganese cyclopentadienone complexes for hydrogenation of carbon dioxide

A series of cobalt and manganese cyclopentadienone complexes are proposed and examined computationally as promising catalysts for hydrogenation of CO₂ to formic acid with total free energies as low as 20.0 kcal mol^-1 in aqueous solution. Density functional theory study of the newly designed cobalt and manganese complexes and experimentally reported iron cyclopentadienone complexes reveals a stepwise hydride transfer mechanism with a water or a methanol molecule assisted proton transfer for the cleavage of H₂ as the rate-determining step.

Aliphatic Mn–PNP complexes for the CO2 hydrogenation reaction: a base free mechanism

Aliphatic amido Mn–PNP-based complexes were found to be promising for CO₂ hydrogenation reaction.

Newly designed manganese and cobalt complexes with pendant amines for the hydrogenation of CO2 to methanol: a DFT study

Computationally designed manganese and cobalt complexes as promising low-cost catalysts for the conversion of CO₂ and H₂ to methanol.