Integrated Machine Learning and Molecular Dynamics for Functional Nanoparticle Design: Synthesis, Characterization, Force-Field Development, and Property Prediction

Abstract

The integration of machine learning (ML) with molecular dynamics (MD) significantly enhances the design of nanoparticles (NPs) across four key areas: synthesis optimization, advanced characterization, ML-based force fields (MLFFs), and property prediction using surrogate models. This review focuses on discrete NPs, excluding extended NPs. Traditional MD FFs often overlook essential polarization and many-body effects, while quantum methods are impractical for larger systems. In contrast, MLFFs bridge the gap between accuracy and scalability, achieving near-DFT precision for trained NP systems at computational costs approaching those of classical MD. Recent studies indicate that ML characterization can provide high morphological accuracy based on experimental imaging, and MLFFs demonstrate a close alignment with quantum reference data. However, challenges like data scarcity, model transferability, and interpretability call for collaborative efforts within the community, including the establishment of standardized benchmarks and open model repositories. This cohesive ML-MD approach enables computationally guided NP discovery for a range of applications in catalysis, energy storage, and biomedicine.