Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo
Abstract
Optimization of atomic coordinates and lattice parameters remains a significant challenge to the wide use of stochastic electronic structure methods such as quantum Monte Carlo (QMC). Measurements of the forces and stress tensor by these methods contain statistical errors, challenging conventional gradient-based numerical optimization methods that assume deterministic results. Additionally, forces are not yet available for some methods, wavefunctions, and basis sets and when available may be expensive to compute to sufficiently high statistical accuracy near energy minima, where the energy surfaces are flat. Here, we explore the use of Gaussian process based techniques to sample the energy surfaces and reduce sensitivity to the statistical nature of the problem. We utilize Latin hypercube sampling, with the number of sampled energy points scaling quadratically with the number of optimized parameters. Furthermore, we show these techniques may be successfully applied to systems consisting of tens of parameters, demonstrating QMC optimization of a benzene molecule starting from a randomly perturbed, broken symmetry geometry.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1480630
- Alternate Identifier(s):
- OSTI ID: 1480165
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 149; Journal Issue: 16; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 74 ATOMIC AND MOLECULAR PHYSICS
Citation Formats
Archibald, Richard K., Krogel, Jaron T., and Kent, Paul R. C. Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo. United States: N. p., 2018.
Web. doi:10.1063/1.5040584.
Archibald, Richard K., Krogel, Jaron T., & Kent, Paul R. C. Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo. United States. https://doi.org/10.1063/1.5040584
Archibald, Richard K., Krogel, Jaron T., and Kent, Paul R. C. Sun .
"Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo". United States. https://doi.org/10.1063/1.5040584. https://www.osti.gov/servlets/purl/1480630.
@article{osti_1480630,
title = {Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo},
author = {Archibald, Richard K. and Krogel, Jaron T. and Kent, Paul R. C.},
abstractNote = {Optimization of atomic coordinates and lattice parameters remains a significant challenge to the wide use of stochastic electronic structure methods such as quantum Monte Carlo (QMC). Measurements of the forces and stress tensor by these methods contain statistical errors, challenging conventional gradient-based numerical optimization methods that assume deterministic results. Additionally, forces are not yet available for some methods, wavefunctions, and basis sets and when available may be expensive to compute to sufficiently high statistical accuracy near energy minima, where the energy surfaces are flat. Here, we explore the use of Gaussian process based techniques to sample the energy surfaces and reduce sensitivity to the statistical nature of the problem. We utilize Latin hypercube sampling, with the number of sampled energy points scaling quadratically with the number of optimized parameters. Furthermore, we show these techniques may be successfully applied to systems consisting of tens of parameters, demonstrating QMC optimization of a benzene molecule starting from a randomly perturbed, broken symmetry geometry.},
doi = {10.1063/1.5040584},
journal = {Journal of Chemical Physics},
number = 16,
volume = 149,
place = {United States},
year = {Sun Oct 28 00:00:00 EDT 2018},
month = {Sun Oct 28 00:00:00 EDT 2018}
}
Web of Science
Figures / Tables:
Works referenced in this record:
Solvent Effects on Excited-State Structures: A Quantum Monte Carlo and Density Functional Study
journal, November 2014
- Guareschi, Riccardo; Floris, Franca Maria; Amovilli, Claudio
- Journal of Chemical Theory and Computation, Vol. 10, Issue 12
Communication: Calculation of interatomic forces and optimization of molecular geometry with auxiliary-field quantum Monte Carlo
journal, May 2018
- Motta, Mario; Zhang, Shiwei
- The Journal of Chemical Physics, Vol. 148, Issue 18
Monte-Carlo solution of Schrödinger's equation
journal, February 1971
- Grimm, R. C.; Storer, R. G.
- Journal of Computational Physics, Vol. 7, Issue 1
Fermion Monte Carlo without fixed nodes: A game of life, death, and annihilation in Slater determinant space
journal, January 2009
- Booth, George H.; Thom, Alex J. W.; Alavi, Ali
- The Journal of Chemical Physics, Vol. 131, Issue 5
Ab initio molecular dynamics simulation of liquid water by quantum Monte Carlo
journal, April 2015
- Zen, Andrea; Luo, Ye; Mazzola, Guglielmo
- The Journal of Chemical Physics, Vol. 142, Issue 14
Construction of reactive potential energy surfaces with Gaussian process regression: active data selection
journal, November 2017
- Guan, Yafu; Yang, Shuo; Zhang, Dong H.
- Molecular Physics, Vol. 116, Issue 7-8
Sequential Exploration of Complex Surfaces Using Minimum Energy Designs
journal, January 2015
- Joseph, V. Roshan; Dasgupta, Tirthankar; Tuo, Rui
- Technometrics, Vol. 57, Issue 1
Stochastic Coupled Cluster Theory
journal, December 2010
- Thom, Alex J. W.
- Physical Review Letters, Vol. 105, Issue 26
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
journal, September 2008
- Gramacy, Robert B.; Lee, Herbert K. H.
- Journal of the American Statistical Association, Vol. 103, Issue 483
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
The Design and Analysis of Computer Experiments
book, January 2003
- Santner, Thomas J.; Williams, Brian J.; Notz, William I.
- Springer Series in Statistics
Bayesian calibration of computer models
journal, August 2001
- Kennedy, Marc C.; O'Hagan, Anthony
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3
Machine learning unifies the modeling of materials and molecules
journal, December 2017
- Bartók, Albert P.; De, Sandip; Poelking, Carl
- Science Advances, Vol. 3, Issue 12
A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo
journal, May 2017
- Zhao, Luning; Neuscamman, Eric
- Journal of Chemical Theory and Computation, Vol. 13, Issue 6
Correlated sampling in quantum Monte Carlo: A route to forces
journal, June 2000
- Filippi, Claudia; Umrigar, C. J.
- Physical Review B, Vol. 61, Issue 24
Electronic quantum Monte Carlo calculations of atomic forces, vibrations, and anharmonicities
journal, June 2005
- Lee, Myung Won; Mella, Massimo; Rappe, Andrew M.
- The Journal of Chemical Physics, Vol. 122, Issue 24
A reactive potential for hydrocarbons with intermolecular interactions
journal, April 2000
- Stuart, Steven J.; Tutein, Alan B.; Harrison, Judith A.
- The Journal of Chemical Physics, Vol. 112, Issue 14
Generalized Latin Hypercube Design for Computer Experiments
journal, November 2010
- Dette, Holger; Pepelyshev, Andrey
- Technometrics, Vol. 52, Issue 4
Energy-consistent pseudopotentials for quantum Monte Carlo calculations
journal, June 2007
- Burkatzki, M.; Filippi, C.; Dolg, M.
- The Journal of Chemical Physics, Vol. 126, Issue 23
Theory of reproducing kernels
journal, March 1950
- Aronszajn, N.
- Transactions of the American Mathematical Society, Vol. 68, Issue 3
Practical Schemes for Accurate Forces in Quantum Monte Carlo
journal, October 2014
- Moroni, S.; Saccani, S.; Filippi, C.
- Journal of Chemical Theory and Computation, Vol. 10, Issue 11
Computing forces with quantum Monte Carlo
journal, September 2000
- Assaraf, Roland; Caffarel, Michel
- The Journal of Chemical Physics, Vol. 113, Issue 10
Theoretical S 1 ← S 0 Absorption Energies of the Anionic Forms of Oxyluciferin by Variational Monte Carlo and Many-Body Green’s Function Theory
journal, August 2017
- Coccia, Emanuele; Varsano, Daniele; Guidoni, Leonardo
- Journal of Chemical Theory and Computation, Vol. 13, Issue 9
Optimizing the Energy with Quantum Monte Carlo: A Lower Numerical Scaling for Jastrow–Slater Expansions
journal, October 2017
- Assaraf, Roland; Moroni, S.; Filippi, Claudia
- Journal of Chemical Theory and Computation, Vol. 13, Issue 11
Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields
journal, November 1994
- Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.
- The Journal of Physical Chemistry, Vol. 98, Issue 45, p. 11623-11627
Accurate emulators for large-scale computer experiments
journal, December 2011
- Haaland, Ben; Qian, Peter Z. G.
- The Annals of Statistics, Vol. 39, Issue 6
Analytic nuclear forces and molecular properties from full configuration interaction quantum Monte Carlo
journal, August 2015
- Thomas, Robert E.; Opalka, Daniel; Overy, Catherine
- The Journal of Chemical Physics, Vol. 143, Issue 5
Nexus: A modular workflow management system for quantum simulation codes
journal, January 2016
- Krogel, Jaron T.
- Computer Physics Communications, Vol. 198
Quantum Monte Carlo Calculations for Minimum Energy Structures
journal, May 2010
- Wagner, Lucas K.; Grossman, Jeffrey C.
- Physical Review Letters, Vol. 104, Issue 21
Geometries of low spin states of multi-centre transition metal complexes through extended broken symmetry variational Monte Carlo
journal, September 2016
- Barborini, Matteo; Guidoni, Leonardo
- The Journal of Chemical Physics, Vol. 145, Issue 12
Computing accurate forces in quantum Monte Carlo using Pulay’s corrections and energy minimization
journal, January 2003
- Casalegno, Mosé; Mella, Massimo; Rappe, Andrew M.
- The Journal of Chemical Physics, Vol. 118, Issue 16
Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
journal, May 1979
- McKay, M. D.; Beckman, R. J.; Conover, W. J.
- Technometrics, Vol. 21, Issue 2
Gaussian Processes for Machine Learning
book, January 2005
- Rasmussen, Carl Edward; Williams, Christopher K. I.
- The MIT Press
The Design and Analysis of Computer Experiments
book, January 2018
- Santner, Thomas J.; Williams, Brian J.; Notz, William I.
- Springer Series in Statistics
Machine learning unifies the modeling of materials and molecules.
text, January 2017
- Bartók, Albert P.; De, Sandip; Poelking, Carl
- Apollo - University of Cambridge Repository
Generalized latin hypercube design for computer experiments
text, January 2009
- Dette, Holger; Pepelyshev, Andrey
- Technische Universität Dortmund
A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo
preprint, January 2017
- Zhao, Luning; Neuscamman, Eric
- arXiv
QMCPACK : An open source ab initio Quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids
text, January 2018
- Kim, Jeongnim; Baczewski, Andrew; Beaudet, Todd D.
- arXiv
Alleviation of the Fermion-sign problem by optimization of many-body wave functions
text, January 2006
- Umrigar, C. J.; Toulouse, Julien; Filippi, Claudia
- arXiv
A Constrained Path Monte Carlo Method for Fermion Ground States
text, January 1996
- Zhang, Shiwei; Carlson, J.; Gubernatis, J. E.
- arXiv
Correlated sampling in quantum Monte Carlo: a route to forces
text, January 1999
- Filippi, Claudia; Umrigar, C. J.
- arXiv
Works referencing / citing this record:
Structural, electronic, and magnetic properties of bulk and epitaxial through diffusion Monte Carlo
journal, December 2019
- Saritas, Kayahan; Krogel, Jaron T.; Okamoto, Satoshi
- Physical Review Materials, Vol. 3, Issue 12
Figures / Tables found in this record: