Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by Fe^IVO

Computational high-throughput screening is an essential tool for catalyst design, limited primarily by the efficiency with which accurate predictions can be made. In bulk heterogeneous catalysis, linear free energy relationships (LFERs) have been extensively developed to relate elementary step activation energies, and thus overall catalytic activity, back to the adsorption energies of key intermediates, dramatically reducing the computational cost of screening. The applicability of these LFERs to single-site catalysts remains unclear, owing to the directional, covalent metal–ligand bonds and the broader chemical space of accessible ligand scaffolds. Through a computational screen of nearly 500 model Fe­(II) complexes for CH₄ hydroxylation, we observe that (1) tuning ligand field strength yields LFERs by comparably shifting energetics of the metal 3d levels that govern the stability of different intermediates and (2) distortion of the metal coordination geometry breaks these LFERs by increasing the splitting between the d_<i>xz</i>/d_<i>yz</i> and d_<i>z</i>² metal states that govern reactivity. Thus, in single-site catalysts, low Brønsted–Evans–Polanyi slopes for oxo formation, which would limit peak turnover frequency achievable through ligand field tuning alone, can be overcome through structural distortions achievable in experimentally characterized compounds. Observations from this screen also motivate the placement of strong HB donors in targeted positions as a scaffold-agnostic strategy for further activity improvement. More generally, our findings motivate broader variation of coordination geometries in reactivity studies with single-site catalysts

Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by Fe^IVO

Computational high-throughput screening is an essential tool for catalyst design, limited primarily by the efficiency with which accurate predictions can be made. In bulk heterogeneous catalysis, linear free energy relationships (LFERs) have been extensively developed to relate elementary step activation energies, and thus overall catalytic activity, back to the adsorption energies of key intermediates, dramatically reducing the computational cost of screening. The applicability of these LFERs to single-site catalysts remains unclear, owing to the directional, covalent metal–ligand bonds and the broader chemical space of accessible ligand scaffolds. Through a computational screen of nearly 500 model Fe­(II) complexes for CH₄ hydroxylation, we observe that (1) tuning ligand field strength yields LFERs by comparably shifting energetics of the metal 3d levels that govern the stability of different intermediates and (2) distortion of the metal coordination geometry breaks these LFERs by increasing the splitting between the d_<i>xz</i>/d_<i>yz</i> and d_<i>z</i>² metal states that govern reactivity. Thus, in single-site catalysts, low Brønsted–Evans–Polanyi slopes for oxo formation, which would limit peak turnover frequency achievable through ligand field tuning alone, can be overcome through structural distortions achievable in experimentally characterized compounds. Observations from this screen also motivate the placement of strong HB donors in targeted positions as a scaffold-agnostic strategy for further activity improvement. More generally, our findings motivate broader variation of coordination geometries in reactivity studies with single-site catalysts

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Virtual high throughput screening, typically driven by first-principles, density functional theory calculations, has emerged as a powerful tool for the discovery of new materials. Although the computational materials science community has benefited from open source tools for the rapid structure generation, calculation, and analysis of crystalline inorganic materials, software and strategies to address the unique challenges of inorganic complex discovery have not been as widely available. We present a unified view of our recent developments in the open source molSimplify code for inorganic discovery. Building on our previous efforts in the automated generation of highly accurate inorganic molecular structures, first-principles simulation, and property analysis to accelerate high-throughput screening, we have recently incorporated a neural network that both improves structure generation and predicts electronic properties prior to first-principles calculation. We also provide an overview of how multimillion molecule organic libraries can be leveraged for inorganic discovery alongside cheminformatics concepts of molecular diversity in order to efficiently traverse chemical space. We demonstrate all of these tools on the discovery of design rules for octahedral Fe­(II/III) redox couples with nitrogen ligands. Over a search of only approximately 40 new molecules, we obtain redox potentials relative to the Fc/Fc⁺ couple ranging from −1 to 4.5 V in aqueous solution. Our new automated correlation analysis reveals heteroatom identity and the degree of structural branching to be key ligand descriptors in determining redox potential. This inorganic discovery toolkit provides a promising approach to advancing transition metal complex design

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Virtual high throughput screening, typically driven by first-principles, density functional theory calculations, has emerged as a powerful tool for the discovery of new materials. Although the computational materials science community has benefited from open source tools for the rapid structure generation, calculation, and analysis of crystalline inorganic materials, software and strategies to address the unique challenges of inorganic complex discovery have not been as widely available. We present a unified view of our recent developments in the open source molSimplify code for inorganic discovery. Building on our previous efforts in the automated generation of highly accurate inorganic molecular structures, first-principles simulation, and property analysis to accelerate high-throughput screening, we have recently incorporated a neural network that both improves structure generation and predicts electronic properties prior to first-principles calculation. We also provide an overview of how multimillion molecule organic libraries can be leveraged for inorganic discovery alongside cheminformatics concepts of molecular diversity in order to efficiently traverse chemical space. We demonstrate all of these tools on the discovery of design rules for octahedral Fe­(II/III) redox couples with nitrogen ligands. Over a search of only approximately 40 new molecules, we obtain redox potentials relative to the Fc/Fc⁺ couple ranging from −1 to 4.5 V in aqueous solution. Our new automated correlation analysis reveals heteroatom identity and the degree of structural branching to be key ligand descriptors in determining redox potential. This inorganic discovery toolkit provides a promising approach to advancing transition metal complex design

Catalytic Performance of Ni Catalysts Supported on α‑, β‑, and γ‑Ga₂O₃ Polymorphs for CO₂ Hydrogenation to Methanol

Nickel-based materials have garnered considerable attention owing to their potential as affordable, effective, and durable catalysts for CO2 hydrogenation. However, their propensity to favor methane production over more desirable methanol has posed a challenge. In this study, we endeavored to address this issue by synthesizing α-, β-, and γ-Ga2O3 supported Ni catalysts through the wet impregnation method. Notably, Ni supported on the α-Ga2O3 catalyst (referred to as 10Ni/α-Ga2O3) exhibited superior activity and methanol selectivity under typical CO2 hydrogenation conditions (3 MPa and 260 °C), reaching ca. 80% methanol selectivity at 0.72% CO2 conversion. This performance outpaced analogous counterparts utilizing β- and γ-Ga2O3 supports, which is attributed to the abundance of strong basic sites inherent in α-Ga2O3. We unveiled the intricate mechanism governing CO2 hydrogenation on 10Ni/α-Ga2O3 catalysts through ex situ characterizations and in situ FTIR. Evidently, H2 underwent dissociation over Ni nanoparticles. It spilts over onto the oxide support, while strong basic sites on the α-Ga2O3 support facilitate the adsorption of CO2, forming bidentate carbonate as a key intermediate and subsequently hydrogenated to yield methanol. Our findings propose a promising avenue for developing cost-effective and highly efficient catalyst systems for methanol synthesis through CO2 hydrogenation