Selected configuration interaction (sCI) methods exploit the sparsity of full (FCI) wave function, yielding significant computational savings and function compression without sacrificing accuracy. Despite recent advances in sCI methods, selection important determinants remains an open problem. We explore possibility utilizing reinforcement learning approaches to solve By mapping problem onto a ...

The performance of an algorithm often critically depends on its parameter configuration. While a variety automated configuration methods have been proposed to relieve users from the tedious and error-prone task manually tuning parameters, there is still lot untapped potential as learned static, i.e., settings remain fixed throughout run. However, it has shown that some parameters are best adjus...