RESUMEN
An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
RESUMEN
Invited for this month's cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.
RESUMEN
Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER, and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in a machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2 N, CaTaO2 N, and SrTaO2 N, and the thermoelectric material Yb14 Mn1 Sb11 . We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn-Sb bonds on the magnetism in Yb14 Mn1 Sb11 . Our contribution enables high-throughput bonding analysis and will facilitate the use of bonding information for machine learning studies.
RESUMEN
Potassium poly (heptazine imide) (K-PHI), a crystalline two-dimensional carbon-nitride material, is an active photocatalyst for water splitting. The potassium ions in K-PHI can be exchanged with other ions to change the properties of the material and eventually to design the catalysts. We report here the electronic structures of several ion-exchanged salts of K-PHI (K, H, Au, Ru, and Mg) and their feasibility as water splitting photocatalysts, which were determined by density functional theory (DFT) calculations. The DFT results are complemented by experiments where the performances in the photocatalytic hydrogen evolution reaction (HER) were recorded. We show that due to its narrow band gap, Ru-PHI is not a suitable photocatalyst. The water oxidation potentials are straddled between the band edge potentials of H-PHI, Au-PHI, and Mg-PHI; thus, these are active photocatalysts for both the oxygen and hydrogen evolution reactions, whereas K-PHI is active only for the HER. The experimental data show that these are active HER photocatalysts, in agreement with the DFT results. Furthermore, Mg-PHI has shown remarkable performance in the HER, with a rate of 539 µmol/(h·g) and a quantum efficiency of 7.14% at 410 nm light irradiation, which could be due to activation of the water molecule upon adsorption, as predicted by our DFT calculations.