The research results of the new material calculation research team of the Computing Center of the Chinese Academy of Sciences are published in the top international journals

Recently, the new material calculation research team of Beijing Computing Center realized the precise design of new 2D MXenes catalytic materials based on the "multi-scale simulation and multi-objective machine learning material calculation and data platform" built on the Beijing Industrial Cloud Platform. Accelerating 2D MXenes Catalyst Discovery for Hydrogen Evolution Reaction by Computer-Driven Workflow and Ensemble Learning Strategy" was published in the top international journal "Journal of Materials Chemistry A" (impact factor: 11.301). The first author is Assistant Researcher Wang Xiaoxu of the Computing Center, and the corresponding author is Professor Su Yanjing of University of Science and Technology Beijing.

Two-dimensional MXenes-OBAs catalytic material discovery framework

Two-dimensional (2D) materials have a large surface area-to-volume ratio and excellent chemical activity, and are considered as potential energy storage and conversion materials. Materials such as graphene and two-dimensional transition metal sulfides have been extensively studied in the electrolysis of water to produce hydrogen, but how to convert in-plane atoms into catalytically active sites is still challenging. The 2D MXenes (M is transition metal, X represents carbon and/or nitrogen) material stripped from the MAX phase are widely used in the fields of energy storage and conversion, electrocatalysis, electromagnetic shielding, and electronic devices. 2D MXenes ordered binary alloy material is a new type of MXenes electrocatalytic multifunctional material. However, because there are more than 70 kinds of master phase MAX, the space for alloy combination is quite large. The traditional trial and error method requires a large number of repeated experiments, the process is cumbersome, the development cycle is long, and the resource consumption is large. Precise synthesis guided by theory is of great significance for the development of new materials and new properties. In recent years, with the substantial increase in computing power, it has become possible to use big data mining and high-throughput computing methods to accelerate the discovery of new materials.



Schematic diagram of machine learning framework