Search

The emerging phase change materials (PCMs) have attracted much attention due to their unique properties and applications. However, the optical properties of PCM at high pressures have not been well explored. In this work, the microstructure and optical properties of GeTe and Sn-substituted GeTe at high pressures up to 30 GPa have been systematically investigated. We find that the substitution of small amounts of Sn for Ge combined with the introduction of high pressure can induce an optical contrast of 117% in crystalline GeTe, which is even higher than the highest optical contrast reported so far (90%) caused by amorphous GeTe to crystalline GeTe. The pressure-induced contrast does not originate from any phase transition as previously, but from the combined effect of Peierls distortion elimination and Ge vacancy formation, which leads to an increase in carrier concentration. The relationship between pressure/atomic composition, Peierls distortion/vacancy formation, and optical contrast can be well explained by a combination of experiments and theoretical calculations. Therefore, this study reveals the effect of high pressure on the optical properties of PCMs and provides new ideas for the design of high-optical-contrast PCMs. [ABSTRACT FROM AUTHOR]

Exploring new therapeutic diseases for existing drugs plays an essential role in reducing drug development costs. However, existing methods for predicting drug–disease association (DDA) lack fusion to multi-neighborhood information, which limits their ability to generalize and forces them to rely on prior knowledge. To this end, we propose a novel DDA model called the Neighborhood Contrastive Learning Heterogeneous Networks (NCH-DDA). NCH-DDA uses both single-neighborhood and multi-neighborhood feature extraction modules to extract important features of drugs and diseases in parallel from multiple potential spaces, such as heterogeneous networks and similarity networks. NCH-DDA fuses single-neighborhood and multi-neighborhood features using contrastive learning to enhance information interaction in different neighborhood spaces, ultimately obtaining universal domain features of drugs and diseases. NCH-DDA uses a combination of predictive loss and triplet loss to reduce dependence on prior knowledge. In different partition schemes of multiple datasets, NCH-DDA achieved the best performance in predicting DDA, outperforming several current state-of-the-art methods. Moreover, NCH-DDA demonstrated better performance in experiments on data sparsity and drug repositioning for Alzheimer's disease, indicating its greater potential in DDA prediction with sparse omics data and drug repositioning applications. • Neighborhood contrastive model for heterogeneous representation and DDA prediction. • MNFEM and SNFEM capture the important features in multiple potential spaces. • Contrast learning aggregated generic neighborhood features of drugs and diseases. • Presented a mixing optimization strategy to reduce the dependence on prior knowledge. • Four of the top five drugs recommended for AD by NCH-DDA were proved to be effective. [ABSTRACT FROM AUTHOR]