Recently, the new materials computing research team of the Beijing Computing Center realized the accurate 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. The related results were published with the title of "Accelerating 2D MXenes Catalyst Discovery for Hydrogen Evolution Reaction by Computer-Driven Workflow and Ensemble Learning Strategy" in the top international journal "Journal of Materials Chemistry A" (impact factor: 11.301). The first author is Wang Xiaoxu, assistant researcher of the Beijing Computing Center, and the corresponding author is Professor Su Yanjing of University of Science and Technology Beijing.

This work combines big data-driven high-throughput computing with machine learning, and integrates high-throughput computing processes and machine learning frameworks as an advanced method to develop efficient 2D MXenes catalysts, and shows in the evaluation of catalytic activity trends and the design of new complex catalysts. It is expected to be extended to a wider range of new material designs.