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.

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

Light-assisted conversion of CO 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 CO reduction reaction (CORR) at an industrial scale. In this work, we employ density functional theory to explore potential tellurium-containing photocathodes for the CORR 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.

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.