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:
-
- Univ. of California, Berkeley, CA (United States). Dept. of Physics
- 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 Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- 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
- Additional Journal Information:
- Journal Volume: 21; Journal Issue: 27; Journal ID: ISSN 1463-9076
- Publisher:
- Royal Society of Chemistry
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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. https://doi.org/10.1039/c9cp02269d
Otis, Leon, and Neuscamman, Eric. Tue .
"Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo". United States. https://doi.org/10.1039/c9cp02269d. https://www.osti.gov/servlets/purl/1567167.
@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},
number = 27,
volume = 21,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2019},
month = {Tue Jan 01 00:00:00 EST 2019}
}
Web of Science
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Works referencing / citing this record:
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