Advancing the design of gold nanomaterials with machine-learned potentials

Abstract Gold nanoparticles (NPs), and their smaller (< 2 nm) counterpart, known as gold nanoclusters (NCs), have emerged in recent years as highly efficient catalysts. They exhibit unique properties, are highly tailorable, and are highly promising for applications in nanomedicine, sensing, and bioimaging. The design of nanomaterials with optimal properties hinges on our ability to understand and control their structure-function relationship, which has remained a challenge so far. The dual organic-metallic nature of ligand-protected Au NCs complicates the experimental characterization of their structure. Density Functional Theory (DFT) calculations are highly accurate but have a high computational cost, making such calculations on large NPs and over long simulation times beyond our reach. Classical simulations allow for a thorough exploration of the configuration space but the empirical force fields they rely on often lack accuracy. In this Topical Review, we discuss recent advances enabled by Machine-Learned Potentials (MLPs), which have the ability to predict energies and atomic forces with DFT-like accuracy for a fraction of the computational cost and can be readily used in molecular simulations. We further show how MLPs have led to the elucidation of the structure, stability, thermodynamics, and reactivity of nanomaterials, thereby paving the way for the accelerated computationally-guided design of Au nanomaterials.