The mastery of details in the workflow of materials machine learning

Abstract

Abstract As machine learning (ML) continues to advance in the field of materials science, the variation in strategies for the same steps of the ML workflow becomes increasingly significant. These details can have a substantial impact on results, yet they have not received the deserved attention. This review aims to explore the various strategies available for each detail within the general workflow of materials ML. Firstly, the general workflow of materials ML will be introduced to help readers gain an understanding of potential details. Subsequently, different strategies of details within each step of the workflow will be presented through state-of-the-art case studies. The potential outcomes associated with choosing different strategies to details will be explored. Following this, suitable strategies for details will be recommended based on distinct application scenarios. Finally, directions for the future development of materials ML concerning details will be proposed. Through these discussions, we aspire to offer a comprehensive understanding of the nuances in details within materials ML. This will serve as valuable reference and guidance for researchers in both materials science and ML.