The augmented potential method: Multiscale modeling toward a spectral defect genome

Modeling of solute chemistry at low-symmetry defects in materials is historically challenging, due to the computation cost required to evaluate thermodynamic properties from first principles. Here, we offer a hybrid multiscale approach called the augmented potential method that connects the chemical flexibility and high accuracy of a universal machine learning potential at the site of the defect, with the computational speed of an efficient potential implemented away from the defect site. The method allows us to rapidly compute distributions of grain boundary segregation energy for 1036 binary alloy pairs (including Ag, Al, Au, Cr, Cu, Fe, Mo, Nb, Ni, Pd, Pt, Ta, V and W solvent), creating a database ∼5x larger than previously published spectral compilations, and yet has improved accuracy. The approach can also address problems such as the solute-solute interactions in polycrystals that require significant computational efforts, paving a pathway toward a complete defect genome in crystalline materials.