Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps-Reference-Cited by-同舟云学术

Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps

Published:2020-04 Issue: Volume:175 Page:109584
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Guénolé Julien^ORCID,Nöhring Wolfram G.,Vaid Aviral,Houllé Frédéric,Xie Zhuocheng,Prakash Aruna^ORCID,Bitzek Erik

Funder

Deutsche Forschungsgemeinschaft

European Research Council

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference72 articles.

1. Practical Methods of Optimization;Fletcher,2013

2. Numerical Optimization;Nocedal,2006

3. Dislocation nucleation in a thin cu film from molecular dynamics simulations: Instability activation by thermal fluctuations;Umeno;Phys. Rev. B,2010

4. Dynamic aspects of dislocation motion: atomistic simulations;Bitzek;Mater. Sci. Eng. A,2005

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