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This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χ_a), the mixing of optically absorbing and nonabsorbing species (χ_o), and the mixing of hygroscopic and nonhygroscopic species (χ_h). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% forχ_a, between 38% and 94% forχ_o, and between 20% and 87% forχ_hwith global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.