A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo
Abstract
We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recentlyintroduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and perprocess memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involving both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multiSlater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, nonperturbative predictions of Mottinsulators’ optical band gaps.
 Authors:
 Univ. of California, Berkeley, CA (United States)
 Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC22)
 OSTI Identifier:
 1376512
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Journal of Chemical Theory and Computation
 Additional Journal Information:
 Journal Volume: 13; Journal Issue: 6; Journal ID: ISSN 15499618
 Publisher:
 American Chemical Society
 Country of Publication:
 United States
 Language:
 English
 Subject:
 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Zhao, Luning, and Neuscamman, Eric. A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo. United States: N. p., 2017.
Web. doi:10.1021/acs.jctc.7b00119.
Zhao, Luning, & Neuscamman, Eric. A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo. United States. doi:10.1021/acs.jctc.7b00119.
Zhao, Luning, and Neuscamman, Eric. Wed .
"A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo". United States.
doi:10.1021/acs.jctc.7b00119. https://www.osti.gov/servlets/purl/1376512.
@article{osti_1376512,
title = {A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo},
author = {Zhao, Luning and Neuscamman, Eric},
abstractNote = {We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recentlyintroduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and perprocess memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involving both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multiSlater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, nonperturbative predictions of Mottinsulators’ optical band gaps.},
doi = {10.1021/acs.jctc.7b00119},
journal = {Journal of Chemical Theory and Computation},
number = 6,
volume = 13,
place = {United States},
year = {Wed May 17 00:00:00 EDT 2017},
month = {Wed May 17 00:00:00 EDT 2017}
}
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