An opinion on machine learning for predicting nanoparticles

Multielement nanoparticles (ME-NPs), composed of five or more elements in roughly equal proportions, exhibit excellent inherent structural, compositional, and site-level diversity alongside exceptional thermal and physical properties, making them promising candidates for catalyst applications. However, traditional experimental methods rely heavily on trial-and-error processes that are time-consuming, inefficient, and cost-prohibitive. Machine learning (ML)/deep learning (DL) have emerged as powerful approaches to address these limitations by enabling scalable mapping between composition, site environment, or structure and ME-NP properties, and by facilitating the design of ME-NPs with targeted functionalities. This work presents a comprehensive review of state-of-the-art ML/DL applications for ME-NPs, covering key algorithms, databases, and evaluation metrics. Special emphasis is placed on property prediction, composition optimization, and phase identification, as well as emerging deep learning techniques. By estimating existing approaches and identifying emerging trends, this review provides a foundation for accelerating the characterization, optimization, and design of ME-NPs. Clinical trial number: Not applicable.