Accelerating Optimizing the Design of Carbon-based Electrocatalyst Via Machine Learning

In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed-loop system. The approach requires at least three parts including high-throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning-based design of carbon-based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.

Automated summary

In the era of artificial intelligence, optimizing current material design methods requires a combination of high-throughput screening, automated synthesis platform, and machine learning algorithms. Through introducing common machine learning algorithms and discussing carbon-based electrocatalyst design, research norms and paper structures have been illustrated.