Discovery of Two-Dimensional Multinary Component Photocatalysts Accelerated by Machine Learning.

Searching for novel and high-performance two-dimensional (2D) materials is an important task for photocatalytic applications. Although multinary compounds exhibit more diversity in structure and properties in comparison to binary 2D materials, they are comparatively under-studied. Herein, using a machine-learning (ML) technique and high-throughput screening, we develop an efficient approach to accurately predict 2D multicomponent photocatalysts.

Unveiling the layer-dependent electronic properties in transition-metal dichalcogenide heterostructures assisted by machine learning.

The electronic properties of layered two-dimensional (2D) transition-metal dichalcogenide (TMD) van der Waals (vdW) heterostructures are strongly dependent on their layer number (N). However, extremely large computational resources are required to investigate the layer-dependent TMD vdW heterostructures for every possible combination if varies in a large range. Fortunately, the machine learning (ML) technique provides a feasible way to probe this problem.

RAB7L1 Participates in Secondary Brain Injury Induced by Experimental Intracerebral Hemorrhage in Rats.

RAB7, a member of RAS oncogene family-like 1 (RAB7L1), is a GTPase belonging to the Rab family and acts as an upstream regulator to regulate the kinase activity of leucine-rich repeat kinase 2 (LRRK2). Although LRRK2 has been shown to aggravate secondary brain injury (SBI) after intracerebral hemorrhage (ICH), it is unknown whether RAB7L1 is also involved in this process. The purpose of the present study was to investigate the role of RAB7L1 in ICH-induced SBI in vivo.