Machine learning prediction of the electronic property of binary transition metal alloys

Machine learning methods have garnered much attention and use in computational catalysis. Previous studies have demonstrated rapid and accurate prediction of a variety of catalytic properties as well as the underlying potential energy landscapes. In particular, d-band center, defined as the first moment of the d-projected density of states, has been widely used as the key descriptor of activity trends for reactions catalyzed by metal surfaces. In this work, we construct a gradient boosting regression (GBR) model for prediction of the d-band center of bulk binary transition metal alloys. An accurate model is obtained using a dataset of over 1200 alloys from the Materials Project database spanning the entire d-block of the periodic table. The d-band centers, periodic groups, and relative compositions of the constituent metals are determined to have the highest feature importance scores, consistent with the underlying physics of the alloy. The regression model presented here offer a promising strategy of rapid property prediction with physical interpretability to aid the optimization and discovery of efficient heterogeneous catalysts.

The Influence of the Oxygen Vacancies on the Pt/TiO2 Single-atom Catalyst – a DFT Study

The titanium dioxide (TiO2) surface is suitable as a substrate for single-atom catalysts(SACs). As a common defect on TiO2, oxygen vacancies may have a significant impact on the adsorption and activity of the adatoms. This work aims to investigate whether titanium dioxide containing surface oxygen vacancies is more suitable as a base material for SACs. This paper calculates the changes in the adsorption energy of Pt atom and the energy of the d-band center on the perfect surface and the surface containing oxygen vacancies. Concerning the perfect surface, the surface containing oxygen vacancies fixes the Pt atom more firmly, and increases the center energy of the d-band of Pt, thereby improving the performance of Pt atom as SACs. Consequently, the (110) surface of Rutile TiO2 with oxygen vacancies may be the best substrate for SACs.

Preparation of Ag0 Nanoparticles by EDM Method as Catalysts for Oxygen Reduction

At present, platinum-based catalysts are the best cathode catalysts, but due to their high prices, they are difficult to use widely. Under alkaline conditions, silver is a better low-cost substitute. Here, a physical preparation method—electrical discharge machining (EDM)—is used to prepare Ag0 nanoparticles. The method is simple and has a high yield. The diameter of prepared nanoparticles is about 30 nm and the nanoparticle surface is rich in defects. These defects enhance the adsorption of O2. In addition, defects can cause tensile strain on the silver catalyst, causing the d-band center of silver to move upward. The defects and the upward shift of the d-band center jointly improve the adsorption energy and catalytic performance of Ag0. This work provides a new method for the engineering construction of surface defects and the preparation of metal catalysts.

The surface of (4) Vesta in visible light as seen by Dawn/VIR

Aims. We analyzed the surface of Vesta at visible wavelengths, using the data of the Visible and InfraRed mapping spectrometer (VIR) on board the Dawn spacecraft. We mapped the variations of various spectral parameters on the entire surface of the asteroid, and also derived a map of the lithology. Methods. We took advantage of the recent corrected VIR visible data to map the radiance factor at 550 nm, three color composites, two spectral slopes, and a band area parameter relative to the 930 nm crystal field signature in pyroxene. Using the howardite-eucrite-diogenite meteorites data as a reference, we derived the lithology of Vesta using the variations of the 930 and 506 nm (spin-forbidden) band centers observed in the VIR dataset. Results. Our spectral parameters highlight a significant spectral diversity at the surface of Vesta. This diversity is mainly evidenced by impact craters and illustrates the heterogeneous subsurface and upper crust of Vesta. Impact craters also participate directly in this spectral diversity by bringing dark exogenous material to an almost entire hemisphere. Our derived lithology agrees with previous results obtained using a combination of infrared and visible data. We therefore demonstrate that it is possible to obtain crucial mineralogical information from visible wavelengths alone. In addition to the 506 nm band, we identified the 550 nm spin-forbidden one. As reported by a laboratory study for synthetic pyroxenes, we also do not observe any shift of the band center of this feature across the surface of Vesta, and thus across different mineralogies, preventing use of the 550 nm spin-forbidden band for the lithology derivation. Finally, the largest previously identified olivine rich-spot shows a peculiar behavior in two color composites but not in the other spectral parameters.