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

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.

Authors:
 [1]; ORCiD logo [2]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Physics
  2. Univ. of California, Berkeley, CA (United States). Dept. of Chemistry; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Chemical Sciences Division
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1567167
Alternate Identifier(s):
OSTI ID: 1529687
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Physical Chemistry Chemical Physics. PCCP (Print)
Additional Journal Information:
Journal Name: Physical Chemistry Chemical Physics. PCCP (Print); Journal Volume: 21; Journal Issue: 27; Journal ID: ISSN 1463-9076
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English

Citation Formats

Otis, Leon, and Neuscamman, Eric. Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo. United States: N. p., 2019. Web. doi:10.1039/c9cp02269d.
Otis, Leon, & Neuscamman, Eric. Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo. United States. doi:10.1039/c9cp02269d.
Otis, Leon, and Neuscamman, Eric. Tue . "Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo". United States. doi:10.1039/c9cp02269d.
@article{osti_1567167,
title = {Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo},
author = {Otis, Leon and Neuscamman, Eric},
abstractNote = {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.},
doi = {10.1039/c9cp02269d},
journal = {Physical Chemistry Chemical Physics. PCCP (Print)},
number = 27,
volume = 21,
place = {United States},
year = {2019},
month = {1}
}

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Works referenced in this record:

General atomic and molecular electronic structure system
journal, November 1993

  • Schmidt, Michael W.; Baldridge, Kim K.; Boatz, Jerry A.
  • Journal of Computational Chemistry, Vol. 14, Issue 11, p. 1347-1363
  • DOI: 10.1002/jcc.540141112