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Modern quantum Monte Carlo (QMC) methods often capture electron correlationthrough both explicitly correlating Jastrow factors and small to mid-sizedconfiguration interaction (CI) expansions. Here, we study the additionaloptimization difficulty created by including increasing numbers of CIparameters. We find evidence that the quality of Variational Mone Carlo (VMC)optimization can be limited by the ability to statistically resolve the CIparameters in the presence of a Jastrow factor. Although using largerstatistical samples can mitigate this issue and bring an optimization closer toits true minimum, this approach is computationally intensive. We presentevidence that similar gains to optimization quality can be had withoutincreasing the sample size by avoiding CI parameters that are statistically themost difficult to resolve. Our findings suggest that, in addition to the valueof using traditional selected configuration interaction (sCI) methods toprepare VMC wave functions, sCI-like methods can play an important role withinVMC by improving the effectiveness of stochastic energy minimization.

Improving Variational Monte Carlo Optimization by Avoiding Statistically Difficult Parameters