Exploiting model uncertainty to improve the scalability of long-time simulations using Parallel Trajectory Splicing-Reference-Cited by-同舟云学术

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Exploiting model uncertainty to improve the scalability of long-time simulations using Parallel Trajectory Splicing

Published:2020-08-10 Issue:6 Volume:28 Page:065015
ISSN:0965-0393
Container-title:Modelling and Simulation in Materials Science and Engineering
language:
Short-container-title:Modelling Simul. Mater. Sci. Eng.

Author:

Garmon Andrew,Perez Danny^ORCID

Funder

U.S. Department of Energy, Office of Science Graduate Student Research

U.S. Department of Energy, Exascale Computing Project

Publisher

IOP Publishing

Subject

Computer Science Applications,Mechanics of Materials,Condensed Matter Physics,General Materials Science,Modeling and Simulation

Link

https://iopscience.iop.org/article/10.1088/1361-651X/aba511/pdf

Reference33 articles.

1. Fast Parallel Algorithms for Short-Range Molecular Dynamics

2. Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table

3. Kinetic activation-relaxation technique: An off-lattice self-learning kinetic Monte Carlo algorithm

4. Long-Time Dynamics through Parallel Trajectory Splicing

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recent advances in Accelerated Molecular Dynamics Methods: Theory and Applications;Comprehensive Computational Chemistry;2024

2. Resource allocation for task-level speculative scientific applications: A proof of concept using Parallel Trajectory Splicing;Parallel Computing;2022-09

3. Uncertainty and anharmonicity in thermally activated dynamics;Computational Materials Science;2021-06

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