A promising intersection of excited-state-specific methods from quantum chemistry and quantum Monte Carlo

We present a discussion of recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo alongside a demonstration of how a combination of methods from these two fields can offer reliably accurate excited state predictions across singly excited, doubly excited, and charge transfer states. Both of these fields have seen important advances supporting excited state simulation in recent years, including the introduction of more effective excited-state-specific optimization methods, improved handling of complicated wave function forms, and ways of explicitly balancing the quality of wave functions for ground and excited states. To emphasize the promise that exists at this intersection, we provide demonstrations using a combination of excited-state-specific complete active space self-consistent field theory, selected configuration interaction, and state-specific variance minimization. These demonstrations show that combining excited-state-specific quantum chemistry and variational Monte Carlo can be more reliably accurate than either equation of motion coupled cluster theory or multi-reference perturbation theory, and that it can offer new clarity in cases where existing high-level methods do not agree.This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsSoftware > Quantum Chemistry

Automated summary

We discuss recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo and show how combining methods from these fields can predict excited states accurately. Important advances in both fields include improved optimization methods, handling of complicated wave function forms, and balancing the quality of wave functions for ground and excited states. Demonstrations using a combination of specific quantum chemistry and variational Monte Carlo show that this approach is more reliable and accurate than other high-level methods and can provide clarity in cases where existing methods do not agree.