Fig. 5: Schematic architecture and performance evaluation of SuperSalt-BO.
From: SuperSalt: equivariant neural network force fields for multicomponent molten salts system
a Illustration of the workflow of SuperSalt-based Bayesian Optimization (SuperSalt-BO) for targeting properties in 11-cation chloride melts. b Target 2 is to identify a quaternary system with a density close to 2.2 g/cm3 at 1200 K in this 11-cation chloride salt system. The initial datasets, consisting of 10 random compositions for both targets, are also shown for reference. c Target 3 is to identify a 5-component system with a mixing enthalpy (EMixing) close to −100 meV/atom at 1200 K in this 11-cation chloride salt system. The initial datasets, consisting of 10 random compositions for both targets, are also shown for reference. “Inter. N” means the N round of iteration of SuperSalt-BO. In both the initial and iterative steps, each upper bar represents the target property value for the corresponding composition shown by the lower colorful bar. Each lower bar denotes a specific composition, which is iteratively adjusted to approach the desired property value.
