The Applications of Machine Learning in Micro‐Nano Materials Research: From High‐Throughput Screening to Intelligent Design

ABSTRACT

Machine learning exhibits significant potential in the research of micro‑nano materials, particularly in accelerating material design and performance optimization through precise structure–property prediction. It is capable of precisely predicting the structure and properties of micro‑nano materials, thereby enabling rational material discovery and minimizing the requirement for time‑consuming and labor‑intensive experiments and iterative trial‑and‑error processes. Micro‑nano materials, including MOFs, two‑dimensional materials, and nanoparticles, possess extremely high specific surface areas and unique size effects, which endow them with distinctive physicochemical properties and multifunctionality unattainable at the macroscopic scale. This review intends to summarize the transformative impact that machine learning has brought to the performance prediction, geometric generation, and intelligent design of micro‑nano materials from the perspective of materials science. Subsequently, its applications in fields such as catalysis, energy, and batteries are presented. Finally, we outline the current limitations and challenges confronted by machine learning and offer projections regarding its future development, with particular emphasis on emerging directions that will further advance the rational design of high‑performance micro‑nano material. This review provides valuable and forward‑looking guidance for future research on machine learning applications in materials science, highlighting the paradigm shift from empirical experimentation to knowledge‑based intelligent design.