Multi-principal element nanoparticles: Synthesis strategies and machine learning prediction

Abstract

Multi-principal element nanoparticles (MPENs) are an emerging class of nano-materials with widespread applications in electrocatalysis owing to their tunable performances and high chemical stability. The extensive chemical compositional space and high surface area become even more significant at the nanoscale level. MPENs exhibit unique properties, including multi-element synergy, high configuration entropy, and long-range atomic ordering with distinct sublattices of semimetallic or metallic components. These characteristics endow MPENs with outstanding catalytic performance and chemical stability, making them promising candidates for high-entropy alloy (HEA). This review details common synthesis approaches for MPENs. The combination of experimental validation with computational preselection provide an efficient method for optimizing MPENs compositions and enhancing their properties for energy-related applications. In addition, we report on the machine-learning (ML) algorithms and review novel ML models related to atomistic simulations and atomic interactions in thermodynamic studies. We also summarize the ML models for macroscale properties, including lattice structures and phase formations. Instances phase formation through ML-derived order parameters and predictive rules is presented to demonstrate the workflow. In addition, we examine research challenges, including ML-guided opposite materials design and uncertainty quantification.