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Title: Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models

Abstract

Abstract The conductor‐like polarizable continuum model (C‐PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in quantum chemistry. We have previously implemented C‐PCM solvation for Hartree‐Fock (HF) and density functional theory (DFT) on graphical processing units (GPUs), enabling the quantum mechanical treatment of large solvated biomolecules. Here, we first propose a GPU‐based algorithm for the PCM conjugate gradient linear solver that greatly improves the performance for very large molecules. The overhead for PCM‐related evaluations now consumes less than 15% of the total runtime for DFT calculations on large molecules. Second, we demonstrate that our algorithms tailored for ground state C‐PCM are transferable to excited state properties. Using a single GPU, our method evaluates the analytic gradient of the linear response PCM time‐dependent density functional theory energy up to 80× faster than a conventional central processing unit (CPU)‐based implementation. In addition, our C‐PCM algorithms are transferable to other methods that require electrostatic potential (ESP) evaluations. For example, we achieve speed‐ups of up to 130× for restricted ESP‐based atomic charge evaluations, when compared to CPU‐based codes. We also summarize and compare the different PCM cavity discretization schemes used in some popular quantum chemistry packages as a reference for bothmore » users and developers.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]
+ Show Author Affiliations
  1. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  3. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1490865
Alternate Identifier(s):
OSTI ID: 1479576
Grant/Contract Number:  
AC02-76SF00515; SC0018906 SciDAC; ACI-1429830ACI-1548562; DE‐SC0018906 SciDAC
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Quantum Chemistry
Additional Journal Information:
Journal Volume: 119; Journal Issue: 1; Journal ID: ISSN 0020-7608
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; excited state; graphical processing unit; nonequilibrium solvation; polarizable continuum

Citation Formats

Liu, Fang, Sanchez, David M., Kulik, Heather J., and Martínez, Todd J. Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models. United States: N. p., 2018. Web. doi:10.1002/qua.25760.
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Liu, Fang, Sanchez, David M., Kulik, Heather J., & Martínez, Todd J. Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models. United States. https://doi.org/10.1002/qua.25760
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Liu, Fang, Sanchez, David M., Kulik, Heather J., and Martínez, Todd J. Thu . "Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models". United States. https://doi.org/10.1002/qua.25760. https://www.osti.gov/servlets/purl/1490865.
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@article{osti_1490865,
title = {Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models},
author = {Liu, Fang and Sanchez, David M. and Kulik, Heather J. and Martínez, Todd J.},
abstractNote = {Abstract The conductor‐like polarizable continuum model (C‐PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in quantum chemistry. We have previously implemented C‐PCM solvation for Hartree‐Fock (HF) and density functional theory (DFT) on graphical processing units (GPUs), enabling the quantum mechanical treatment of large solvated biomolecules. Here, we first propose a GPU‐based algorithm for the PCM conjugate gradient linear solver that greatly improves the performance for very large molecules. The overhead for PCM‐related evaluations now consumes less than 15% of the total runtime for DFT calculations on large molecules. Second, we demonstrate that our algorithms tailored for ground state C‐PCM are transferable to excited state properties. Using a single GPU, our method evaluates the analytic gradient of the linear response PCM time‐dependent density functional theory energy up to 80× faster than a conventional central processing unit (CPU)‐based implementation. In addition, our C‐PCM algorithms are transferable to other methods that require electrostatic potential (ESP) evaluations. For example, we achieve speed‐ups of up to 130× for restricted ESP‐based atomic charge evaluations, when compared to CPU‐based codes. We also summarize and compare the different PCM cavity discretization schemes used in some popular quantum chemistry packages as a reference for both users and developers.},
doi = {10.1002/qua.25760},
journal = {International Journal of Quantum Chemistry},
number = 1,
volume = 119,
place = {United States},
year = {Thu Oct 11 00:00:00 EDT 2018},
month = {Thu Oct 11 00:00:00 EDT 2018}
}
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https://doi.org/10.1002/qua.25760
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Cited by: 30 works
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Figures / Tables:

Table 1 Table 1: Combinations of tessellation schemes (rows) and cavity types (columns) in C-PCM implementations for some representative quantum chemistry software packages. Abbreviations used are: GAMESS (GM), Gaussian (GS), NWChem (NW), ORCA (OR), Q-CHEM (QC) and TERACHEM (TC).
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  • DOI: 10.1021/ct800526s

The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices
journal, January 1975

A fast algorithm for particle simulations
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Efficient molecular numerical integration schemes
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Dense linear algebra solvers for multicore with GPU accelerators
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  • DOI: 10.1109/IPDPSW.2010.5470941

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  • Nature Communications, Vol. 11, Issue 1
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Non-covalent double bond sensors for gas-phase infrared spectroscopy of unsaturated fatty acids
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    Numerical assessment for accuracy and GPU acceleration of TD-DMRG time evolution schemes
    journal, January 2020

    • Li, Weitang; Ren, Jiajun; Shuai, Zhigang
    • The Journal of Chemical Physics, Vol. 152, Issue 2
    • DOI: 10.1063/1.5135363

    On combining the conductor-like screening model and optimally tuned range-separated hybrid density functionals
    journal, May 2019

    • Sachse, Torsten; Martínez, Todd J.; Presselt, Martin
    • The Journal of Chemical Physics, Vol. 150, Issue 17
    • DOI: 10.1063/1.5064730
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