Effective Atom Theory: Gradient-Driven ab initio Materials Design
Justin Tahmassebpur, Brandon Li, Boris Barron, Héctor Abruña, Peter Frazier, Tomás Arias
公開日: 2025/9/8
Abstract
We introduce Effective Atom Theory (EAT), a framework that transforms combinatorial materials design into a smooth, gradient-driven optimization within density functional theory (DFT). Atoms are represented as probabilistic mixtures of elements, enabling gradient-based optimizers to converge to a physically realizable material in about 50 energy evaluations -- far fewer than combinatorial optimization methods. Applied to Co-Cr-Ni-V oxides for the alkaline oxygen evolution reaction (OER), EAT leads to a final recommended composition of Co0.19Cr0.06V0.31Ni0.44O.