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Exploring density functional subspaces with genetic algorithms
Gastegger, Michael; González, Leticia; Marquetand, Philipp
We use a genetic algorithm to explore the subspace of combination and parametrization patterns spanned by a set of popular exchange and correlation functional approximations. Using the well-balanced GMTKN30 benchmark database to guide the evolutionary process, we find that the genetic algorithm is able to recover variants of several popular generalized gradient approximation functionals and hybrid functionals. For the latter class, the algorithm is able to identify a reparametrized version of the three-parameter hybrid B3PW91, which shows significantly improved performance compared to conventional versions of B3PW91. Furthermore, the possible application of this algorithm to automatically construct so-called “niche”-functionals—specially tailored to specific applications—is demonstrated.
