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Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials

Liu, Kuang and Tiwari, Subodh and Sheng, Chunyang and Krishnamoorthy, Aravind and Hong, Sungwook and Rajak, Pankaj and Kalia, Rajiv K. and Nakano, Aiichiro and Nomura, Ken-ichi and Vashishta, Priya and Kunaseth, Manaschai and Naserifar, Saber and Goddard, William A., III and Luo, Ye and Romero, Nichols A. and Shimojo, Fuyuki (2018) Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials. In: 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA). IEEE , Piscataway, NJ, pp. 41-48. ISBN 978-1-7281-0176-7. http://resolver.caltech.edu/CaltechAUTHORS:20190423-124212621

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Abstract

Reactive molecular dynamics is a powerful simulation method for describing chemical reactions. Here, we introduce a new generalized polarizable reactive force-field (ReaxPQ+) model to significantly improve the accuracy by accommodating the reorganization of surrounding media. The increased computation is accelerated by (1) extended Lagrangian approach to eliminate the speed-limiting charge iteration, (2) shift-collapse computation of many-body renormalized n-tuples, which provably minimizes data transfer, (3) multithreading with round-robin data privatization, and (4) data reordering to reduce computation and allow vectorization. The new code achieves (1) weak-scaling parallel efficiency of 0.989 for 131,072 cores, and (2) eight-fold reduction of time-to-solution (T2S) compared with the original code, on an Intel Knights Landing-based computer. The reduced T2S has for the first time allowed purely computational synthesis of atomically-thin transition metal dichalcogenide layers assisted by machine learning to discover a novel synthetic pathway.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ScalA.2018.00009 DOIArticle
https://sc18.supercomputing.org/proceedings/workshops/workshop_files/ws_lasalss106s2-file1.pdfOrganizationConference Paper
ORCID:
AuthorORCID
Naserifar, Saber0000-0002-1069-9789
Goddard, William A., III0000-0003-0097-5716
Additional Information:© 2018 IEEE. This work was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0014607. An award of computer time was provided by the Aurora Early Science Program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0014607
Department of Energy (DOE)DE-AC02-06CH11357
Subject Keywords:Applications/Computational materials science and engineering; Algorithms/Hybrid/heterogeneous/accelerated algorithms and other high-performance algorithms
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1306
Record Number:CaltechAUTHORS:20190423-124212621
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190423-124212621
Official Citation:K. Liu et al., "Shift-Collapse Acceleration of Generalized Polarizable Reactive Molecular Dynamics for Machine Learning-Assisted Computational Synthesis of Layered Materials," 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA), Dallas, TX, USA, 2018, pp. 41-48. doi: 10.1109/ScalA.2018.00009
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94893
Collection:CaltechAUTHORS
Deposited By: Donna Wrublewski
Deposited On:10 May 2019 16:38
Last Modified:10 May 2019 18:03

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