Propane dehydrogenation catalysis of group IIIB and IVB metal hydrides.

Catalytic propane dehydrogenation (PDH) has mainly been studied using metal- and metal oxide-based catalysts. Studies on dehydrogenation catalysis by metal hydrides, however, have rarely been reported. In this study, PDH reactions using group IIIB and IVB metal hydride catalysts were investigated under relatively low-temperature conditions of 450 °C. Lanthanum hydride exhibited the lowest activation energy for dehydrogenation and the highest propylene yield. Based on kinetics studies, a comparison between the reported calculation results and isotope experiments, the hydrogen vacancies of metal hydrides were involved in low-temperature PDH reactions.

Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning.

The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structures and surface termination planes. Moreover, we extend the model to naturally include multiple-cation effects and transfer it to ternary oxides. The present approach enables the band alignment of a vast number of solid surfaces, thereby opening the way to a systematic understanding and materials screening.