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Path Space Markov Chain Monte Carlo Methods for Molecular Simulation

Citation

Rosa-Raíces, Jorge Luis (2022) Path Space Markov Chain Monte Carlo Methods for Molecular Simulation. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/10jr-hg67. https://resolver.caltech.edu/CaltechTHESIS:03292022-234015177

Abstract

Path space Markov-chain Monte Carlo (McMC) provides a versatile framework for simulating the structure and dynamics of condensed-phase systems aptly described by classical and quantum Boltzmann statistics. This thesis comprises our efforts to design, analyze and improve path space McMC algorithms to achieve numerically advantageous, and physically accurate, simulation of molecular processes across a range of scales. To improve molecular dynamics (MD) simulations of atomically resolved systems exhibiting pronounced nuclear quantum effects, we introduce a family of integrators for non-preconditioned path-integral MD exhibiting dimension-free statistical accuracy and efficiency, and enabling a many-fold increase in time-step stability relative to conventional approaches at no additional computational cost or implementation complexity. The integrators come with robust performance guarantees that are borne out in thermostatted ring-polymer MD simulations of realistic condensed-phase models. Concurrently, toward extending the range of accessible timescales in stochastic MD simulations of mesoscale coarse-grained molecular systems, we introduce a parallel-in-time integrator for the overdamped Langevin equation based on McMC evaluation of a path-integral representation of the many time-step stochastic MD transition kernel. The parallel-in-time integrator achieves simultaneous integration of multiple stochastic MD time-steps at no greater wall-time cost and with no lesser accuracy than a standard Euler--Maruyama integrator does in serial, and thus instantiates new opportunities to accelerate stochastic dynamics simulations on massively parallel computer architectures. Our work along these two methodological avenues extends the utility of path space McMC across applications in molecular simulation and has broader implications in other disciplines that require accurate and efficient simulations of Markov diffusion processes in state spaces or path spaces.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Markov chain Monte Carlo, path integrals, molecular dynamics integrators, parallel-in-time integration, dimension-free accuracy
Degree Grantor:California Institute of Technology
Division:Chemistry and Chemical Engineering
Major Option:Chemistry
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Miller, Thomas F. (advisor)
  • Stoltz, Brian M. (co-advisor)
Group:Miller Group
Thesis Committee:
  • Wang, Zhen-Gang (chair)
  • Okumura, Mitchio
  • Miller, Thomas F.
  • Stuart, Andrew M.
  • Stoltz, Brian M.
Defense Date:28 April 2022
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-FOA-0001912
Department of Energy (DOE)DE-AC02-05CH11231
Department of Energy (DOE)DE-SC0019390
Office of Naval Research (ONR)N00014-10-1-0884
NSFDMS-1816378
NIHR01GM125063
Record Number:CaltechTHESIS:03292022-234015177
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:03292022-234015177
DOI:10.7907/10jr-hg67
Related URLs:
URLURL TypeDescription
https://doi.org/10.1063/1.5125455DOIPath-accelerated molecular dynamics (Published article in J. Chem. Phys.)
https://doi.org/10.1063/1.5134810DOIDimension-free non-preconditioned path-integral molecular dynamics (Published article in J. Chem. Phys.)
https://doi.org/10.1063/5.0036954DOIGeneralized integrators for dimension-free thermostatted ring-polymer molecular dynamics integrators (Published article in J. Chem. Phys.)
http://dx.doi.org/10.22002/D1.20111DOISupplementary data to Path-accelerated molecular dynamics article (DOI: 10.1063/1.5125455)
ORCID:
AuthorORCID
Rosa-Raíces, Jorge Luis0000-0003-2311-2948
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14534
Collection:CaltechTHESIS
Deposited By: Jorge Rosa
Deposited On:17 May 2022 18:29
Last Modified:13 Jul 2022 22:52

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