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Abstract The synthesis of multielemental nanoparticles with size- and shape-control has been studied over the last several decades due to their diverse applications in catalysis, plasmonics, solar cells, electronics, and even as theranostics. While solution-phase methods afford remarkable structural control in mono- and bimetallic systems, extending comparable control to multielemental nanostructures remains largely intractable. This limitation stems from an incomplete understanding of colloidal reaction mechanisms, compounded by disparities among multiple precursors in solubility, redox behavior, and ligand coordination. Consequently, the identification of transferable design rules or retrosynthetic strategies for multielemental nanomaterials has remained elusive. Moreover, intermediates formed during synthesis often compete with the final product, resulting in impurity structures. In a few instances, multielemental compositions have been achieved using conventional solution syntheses; however, these approaches are often difficult to extend to other systems. Atom-by-atom construction strategies such as synchronized precursor reduction using potent reducing agents or rapid tandem precursor decomposition have enabled multielement incorporation, yet they are poorly suited for accessing diverse particle shapes or for tailoring surface ensembles, both of which are critical for dictating facet-dependent properties in applications such as catalysis. Our perspective argues that nanoparticle conversion strategies, such as thermal diffusion, cation exchange, and galvanic replacement, are not simply alternative synthetic routes, but are processes that enable a distinct framework for retrosynthetic design of multielemental nanoparticles. In these approaches, transformations proceed from preformed precursor nanoparticles, which act as structural and chemical templates for subsequent evolution. These precursor nanoparticles guide the composition and shape of the product nanoparticles, providing a means to decouple precursor incompatibilities from final structure. In this context, chemical and structural selectivity emerge from the interplay of diffusion, interfacial energetics, and lattice constraints, rather than solely from precursor reactivity. This perspective discusses how nanoparticle conversion chemistry may enable increasingly predictive syntheses of complex multielemental nanoparticles. 

High-entropy alloys (HEAs), characterized as compositionallycomplex solid solutions with five or more metal elements, have emerged as a novelclass of catalytic materials with unique attributes. Because of the remarkablediversity of multielement sites or site ensembles stabilized by configurationalentropy, human exploration of the multidimensional design space of HEAspresents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-basedmodeling. Leveraging deep learning interatomic potentials for large-scalemolecular simulations and pretrained machine learning models of surfacereactivity, our approach effectively rationalizes the enhanced activity of apreviously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemicaloxygen reduction, as corroborated by experimental observations. We contend thatthis framework deepens our fundamental understanding of the surface reactivity ofhigh-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatilematerial platform for catalyzing sustainable chemical and energy transformations.