research-article

A scalable method for ab initio computation of free energies in nanoscale systems

Authors:

D. M. Nicholson,

T. C. SchulthessAuthors Info & Claims

SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

Article No.: 64, Pages 1 - 8

https://doi.org/10.1145/1654059.1654125

Published: 14 November 2009 Publication History

Abstract

Calculating the thermodynamics of nanoscale systems presents challenges in the simultaneous treatment of the electronic structure, which determines the interactions between atoms, and the statistical fluctuations that become ever more important at shorter length scales. Here we present a highly scalable method that combines ab initio electronic structure techniques, we use the Locally Self-Consitent Multiple Scattering (LSMS) technique, with the Wang-Landau (WL) algorithm to compute free energies and other thermodynamic properties of nanoscale systems. The combined WL-LSMS code is targeted to the study of nanomagnetic systems that have anywhere from about one hundred to a few thousand atoms. The code scales very well on the Cray XT5 system at ORNL, sustaining 1.03 Petaflop/s in double precision on 147,464 cores.

References

[1]

Binder, K., and Landau, D. P. Phys. Rev. B 30 (1984), 1477.

[2]

Hohenberg, P., and Kohn, W. Phys. Rev. 136 (1964), 864.

[3]

Kent, P. R. C. Computational challenges of largescale, long-time, first-principles molecular dynamics. J. of Physics: Conf. Ser. 125 (2008), 012054.

[4]

Kohn, W., and Sham, L. Phys. Rev. 140 (1965), 1133.

[5]

Laio, A., and Parrinello, M. Escaping free-energy minima. PNAS 99 (2002), 12562--12566.

[6]

Martin, R. M. Electronic Structure: Basic Theory and Practical Methods. Cambridge, 2004.

[7]

Nicholson, D. M. C., and Brown, R. H. Phys. Rev. B 67 (2003), 0164011.

[8]

Nicholson, D. M. C., Stocks, G. M., and Y Wang, E. A. Phys. Rev. B 50 (1994), 14686.

[9]

Prodan, E., and Kohn, W. Proc. Natl. Acad. Sci. 102 (2005), 11635.

[10]

Ujfalussy, B., Wang, X., and D. M. C. Nicholson, E. A. J. Appl. Phys. 85 (1999), 4824.

[11]

Wang, F., and Landau, D. P. Phys. Rev. Lett. 86 (2001), 2050.

[12]

Wang, S., Mitchell, S. J., and Rikvold, P. A. Ab initio monte carlo simulations for nanoscopic lithium systems at different temperatures. Comput. Mat. Sci. 29 (2004), 145.

[13]

Wang, Y., Stocks, G. M., and W. A. Shelton, E. A. Phys. Rev. Lett. 75 (1995), 2867.

[14]

Zhou, C., Schulthess, T. C., and Mryasov, O. Magnetic anisotropy of fept nanoparticles: temperature-dependent free energy barrier for switching. 2950--2952.

[15]

Zhou, C., Schulthess, T. C., Torbrgge, S., and Landau, D. P. Wang-landau algorithm for continuous models and joint density of states. Phys. Rev. Lett. 96 (2006), 120201.

Cited By

Razakh TLinker TLuo YKalia RNomura KVashishta PNakano A(2024)Accelerating Quantum Light-Matter Dynamics on Graphics Processing Units2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00176(1057-1066)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00176
Piroozan NKumar N(2023)Enabling Performant Thermal Conductivity Modeling with DeePMD and LAMMPS on CPUsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3626086(88-94)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3626086
Anand GGhosh SZhang LAnupam AFreeman COrtner CEisenbach MKermode J(2022)Exploiting Machine Learning in Multiscale Modelling of MaterialsJournal of The Institution of Engineers (India): Series D10.1007/s40033-022-00424-z104:2(867-877)Online publication date: 28-Nov-2022
https://doi.org/10.1007/s40033-022-00424-z
Show More Cited By

Index Terms

A scalable method for ab initio computation of free energies in nanoscale systems

Recommendations

Ab initio Tertiary Structure Prediction of Proteins

A daunting challenge in the area of computational biology has been to develop a method to theoretically predict the correct three-dimensional structure of a protein given its linear amino acid sequence. The ability to surmount this challenge, which is ...
Ab initio modelling of the N-terminal domain of the secretin receptors

G protein coupled receptors of the secretin family are activated by peptide hormones of about 30 residues in length. There is considerable sequence homology within both the hormone and receptor families. The receptors possess in addition to the integral ...
Ab initio study of chiral recognition of β-butyrolactone by cyclodextrins
ICCS'06: Proceedings of the 6th international conference on Computational Science - Volume Part III

Separation of stereoisomers of organic compounds is an important and challenge task for chemists. Cyclodextrins and their derivatives have been widely used in chromatography for this application. Experimental results indicated that substituents on the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

November 2009

778 pages

ISBN:9781605587448

DOI:10.1145/1654059

Conference Chair:
Wilfred Pinfold

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

SC '09

Sponsor:

SIGARCH
IEEE-CS

SC '09: International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 20, 2009

Oregon, Portland

Acceptance Rates

SC '09 Paper Acceptance Rate 59 of 261 submissions, 23%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
48
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Razakh TLinker TLuo YKalia RNomura KVashishta PNakano A(2024)Accelerating Quantum Light-Matter Dynamics on Graphics Processing Units2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00176(1057-1066)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00176
Piroozan NKumar N(2023)Enabling Performant Thermal Conductivity Modeling with DeePMD and LAMMPS on CPUsProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3626086(88-94)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3626086
Anand GGhosh SZhang LAnupam AFreeman COrtner CEisenbach MKermode J(2022)Exploiting Machine Learning in Multiscale Modelling of MaterialsJournal of The Institution of Engineers (India): Series D10.1007/s40033-022-00424-z104:2(867-877)Online publication date: 28-Nov-2022
https://doi.org/10.1007/s40033-022-00424-z
Lu DWang HChen MLin LCar RE WJia WZhang L(2021)86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracyComputer Physics Communications10.1016/j.cpc.2020.107624259(107624)Online publication date: Mar-2021
https://doi.org/10.1016/j.cpc.2020.107624
Archibald RChow ED’Azevedo EDongarra JEisenbach MFebbo RLopez FNichols DTomov SWong KYin J(2020)Integrating Deep Learning in Domain Sciences at ExascaleDriving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI10.1007/978-3-030-63393-6_3(35-50)Online publication date: 18-Dec-2020
https://doi.org/10.1007/978-3-030-63393-6_3
Das SMotamarri PGavini VTurcksin BLi YLeback BTaufer MBalaji PPeña A(2019)Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computingProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3357157(1-11)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3357157
Eisenbach MPei ZLiu X(2019)First-principles study of order-disorder transitions in multicomponent solid-solution alloysJournal of Physics: Condensed Matter10.1088/1361-648X/ab13d831:27(273002)Online publication date: 26-Apr-2019
https://doi.org/10.1088/1361-648X/ab13d8
Li YEisenbach M(2017)A Histogram-Free Multicanonical Monte Carlo Algorithm for the Basis Expansion of Density of StatesProceedings of the Platform for Advanced Scientific Computing Conference10.1145/3093172.3093235(1-7)Online publication date: 26-Jun-2017
https://dl.acm.org/doi/10.1145/3093172.3093235
Eisenbach MLarkin JLutjens JRennich SRogers J(2016)GPU Acceleration of the Locally Selfconsistent Multiple Scattering Code for First Principles Calculation of the Ground State and Statistical Physics of MaterialsBig Data Technology and Applications10.1007/978-981-10-0457-5_24(259-268)Online publication date: 2-Feb-2016
https://doi.org/10.1007/978-981-10-0457-5_24
Cassidy AAlvarez-Icaza RAkopyan FSawada JArthur JMerolla PDatta PTallada MTaba BAndreopoulos AAmir AEsser SKusnitz JAppuswamy RHaymes CBrezzo BMoussalli RBellofatto RBaks CMastro MSchleupen KCox CInoue KMillman SImam NMcQuinn ENakamura YVo IGuo CNguyen DLekuch SAsaad SFriedman DJackson BFlickner MRisk WManohar RModha DDamkroger TDongarra J(2014)Real-time scalable cortical computing at 46 giga-synaptic OPS/watt with ~100× speedup in time-to-solution and ~100,000× reduction in energy-to-solutionProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC.2014.8(27-38)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/SC.2014.8
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents