Agentic Discovery of Exchange–Correlation Density Functionals

Titouan Duston, Jiashu Liang, Henry Wang, Weihao Gao, Xuelan Wen, Nan Sheng, Weiluo Ren, Yang Sun, Yixiao Chen

Abstract:

The development of accurate exchange–correlation (XC) functionals remains a longstanding
challenge in density functional theory (DFT). The vast majority of XC functionals have been hand
designed by human researchers combining physical insight, exact constraints, and empirical fitting.
Recent advances in large language models enable a systematic, automated alternative to this
human-driven design loop. This report presents an agentic search system in which an LLM proposes
structured functional-form changes guided by evolutionary history. The system attempts to improve
functional performance through an iterative plan–execute–summarize loop, where improvements
are measurable by optimizing functional parameters against a standard thermochemistry dataset,
then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a
(Seed Agentic Functional Search 2026), improves upon the gold-standard ωB97M-V baseline by
∼9%. These results also surface a cautionary lesson for AI-assisted science: models powerful
enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts
to game the benchmark; domain expertise translated into explicitly enforced constraints remains
essential to keeping results scientifically grounded.