  Systematic development of accurate density functionals has been a
decades-long challenge for scientists. Despite the emerging application of
machine learning (ML) in approximating functionals, the resulting ML
functionals usually contain more than tens of thousands parameters, which makes
a huge gap in the formulation with the conventional human-designed symbolic
functionals. We propose a new framework, Symbolic Functional Evolutionary
Search (SyFES), that automatically constructs accurate functionals in the
symbolic form, which is more explainable to humans, cheaper to evaluate, and
easier to integrate to existing density functional theory codes than other ML
functionals. We first show that without prior knowledge, SyFES reconstructed a
known functional from scratch. We then demonstrate that evolving from an
existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google
Accelerated Science 22), that performs better for the majority of molecular
types in the test set of Main Group Chemistry Database (MGCDB84). Our framework
opens a new direction in leveraging computing power for the systematic
development of symbolic density functionals.
