10.18126/M2J63D
Shea, Jacqueline A. R.
Jacqueline A. R.
Shea
Neuscamman, Eric
Eric
Neuscamman
Size consistent excited states via algorithmic transformations between variational principles
Materials Data Facility
2017
Dataset
QMCPACK
Monte Carlo
variational Monte Carlo
QMC
VMC
Eric Neuscamman (eneuscamman@berkeley.edu)
eneuscamman@berkeley.edu
2017-10-20T15:23:45Z
2017-10-20T15:23:45Z
2017-10-20
https://arxiv.org/abs/1708.09805
We demonstrate that a broad class of excited state variational principles is not size consistent. In light of this difficulty, we develop and test an approach to excited state optimization that transforms between variational principles in order to achieve state selectivity, size consistency, and compatibility with quantum Monte Carlo. To complement our formal analysis, we provide numerical examples that confirm these properties and demonstrate how they contribute to a more black box approach to excited states in quantum Monte Carlo.