Machine learning applications in nanomaterials: Recent advances and future perspectives

Abstract

Nanomaterials demonstrate enormous potential applications in various scientific and engineering fields due to their unique physical and chemical properties. With the rapid development of machine learning (ML) technology, its role in the application of nanomaterials is becoming increasingly prominent. Nanomaterials, assisted by various ML algorithms, efficiently model their structure–property relationships, enabling precise prediction and rational design. This review aims to explore the state-of-the-art and future trends of ML in nanomaterial research. It focuses on analyzing research strategies for ML-assisted nanomaterials, including design, characterization, and preparation strategies. The review systematically examines research outcomes in property prediction, structure optimization, synthesis design, characterization analysis, image processing, and quality control, while also summarizing and looking ahead to future development directions. The ML not only accelerates the discovery and development of nanomaterials but also enhances the understanding of nanoscale phenomena, broadens the practical applications of nanoscience, and provides new ideas and technological means for intelligent, high-throughput nanomaterial research and development.