Assessing the Accuracy of Density Functional Approximations for Predicting Hydrolysis Reaction Kinetics

Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new data set, Barrier Heights for HydrOlysis - 36 (BH2O-36), to enable the design of density functional approximations (DFAs) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers ΔE^‡ calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), ωB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (nonhybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for long-range interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this data set.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Data-Driven Investigation of Tellurium-Containing Semiconductors for CO2 Reduction: Trends in Adsorption and Scaling Relations

Light-assisted conversion of CO₂ into liquid fuels is one of several possible approaches to combating the rise of carbon dioxide emissions. Unfortunately, there are currently no known materials that are efficient, selective, or active enough to facilitate the photocatalytic CO₂ reduction reaction (CO₂RR) at an industrial scale. In this work, we employ density functional theory to explore potential tellurium-containing photocathodes for the CO₂RR by observing trends in adsorption properties arising from over 350 *H, 200 *CO, and 110 *CHO surface-adsorbate structures spanning 39 surfaces of 11 materials. Our results reveal a scaling relationship between *CHO and *H chemisorption energies and charge transfer values, while the scaling relation (typically found in transition metals) between *CO and *CHO adsorption energies is absent. We hypothesize the scaling relation between *H and *CHO to be related to the lone electron located on the bonding carbon atom, while the lack of scaling relation in *CO we attribute to the ability of the lone pair on the C atom to form multiple bonding modes. We compute two predominant orbital-level interactions in the *CO-surface bonds (either using s or p orbitals) in addition to bonding modes involving both σ and π interactions using the Crystal Orbital Hamiltonian Population analysis. We demonstrate that bonds involving the C s orbital are more chemisorptive than the C p orbitals of CO. In general, chemisorption trends demonstrate decreased *H competition with respect to *CO adsorption and enhanced *CHO stability. Finally, we uncover simple element-specific design rules with Te, Se, and Ga sites showing increased competition and Zn, Yb, Rb, Br, and Cl sites showing decreased competition for hydrogen adsorption. We anticipate that these trends will help further screen these materials for potential CO₂RR performance.

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO₂ reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.