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Issue 27, 2019
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Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo

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Abstract

We present a comparison between a number of recently introduced low-memory wave function optimization methods for variational Monte Carlo in which we find that first and second derivative methods possess strongly complementary relative advantages. While we find that low-memory variants of the linear method are vastly more efficient at bringing wave functions with disparate types of nonlinear parameters to the vicinity of the energy minimum, accelerated descent approaches are then able to locate the precise minimum with less bias and lower statistical uncertainty. By constructing a simple hybrid approach that combines these methodologies, we show that all of these advantages can be had at once when simultaneously optimizing large determinant expansions, molecular orbital shapes, traditional Jastrow correlation factors, and more nonlinear many-electron Jastrow factors.

Graphical abstract: Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo

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Article information


Submitted
22 Apr 2019
Accepted
18 Jun 2019
First published
19 Jun 2019

Phys. Chem. Chem. Phys., 2019,21, 14491-14510
Article type
Perspective
Author version available

Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo

L. Otis and E. Neuscamman, Phys. Chem. Chem. Phys., 2019, 21, 14491
DOI: 10.1039/C9CP02269D

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