Toward Automated Computational Discovery of Battery Materials

New rechargeable batteries with high energy density and low cost have been intensively explored, but their commercialization still faces multiple challenges involving battery materials and interfaces. Some difficulties faced by battery materials are that a single material often needs to possess multiple functions, and also needs to be cheap, easy to prepare, safe, and environmentally friendly. Recent developments in workflow managers (WMs) along with continuously increasing computing power have enabled the automated computational workflow method. Using this method, the WM can execute the predesigned research workflow to study tens of thousands of materials and screen out materials that meet the multiple requirements. In this perspective, a critical overview of the automated computational workflows is presented, focusing on the high-throughput study of battery materials. First, an introduction to the automated computational workflow as well as commonly used WMs will be given. Next, the latest works and methods to build such automated workflows are presented. Finally, an outlook on the existing challenges and future directions to drive computational and experimental developments in this nascent field is provided.

Automated summary

This article discusses the application of automated computational workflows in high-throughput research of battery materials. It first introduces the concept of automated computational workflows and commonly used workflow managers, then presents the latest methods and works to build such automated workflows, and provides an outlook on the existing challenges and future directions in this field.