StructOpt: A modular materials structure optimization suite incorporating experimental data and simulated energies AITranslate
Abstract AITranslate
Highlights • StructOpt is an atomic optimization software package that uses genetic algorithms. • StructOpt determines atomic structures using multiobjective optimization. • StructOpt focuses on electron microscopy experimental data and simulated energies. • StructOpt can integrate already-existing experimental data simulations. • StructOpt’s code is open source and available on GitHub. StructOpt, an open-source structure optimization suite, applies genetic algorithm and particle swarm methods to obtain atomic structures that minimize an objective function. The objective function typically consists of the energy and the error between simulated and experimental data, which is typically applied to determine structures that minimize energy to the extent possible while also being fully consistent with available experimental data. We present example use cases including the structure of a metastable Pt nanoparticle determined from energetic and scanning transmission electron microscopy data, and the structure of an amorphous-nanocrystal composite determined from energetic and fluctuation electron microscopy data. StructOpt is modular in its construction and therefore is naturally extensible to include new materials simulation modules or new optimization methods, either written by the user or existing in other code packages. It uses the Message Passing Interface’s (MPI) dynamic process management functionality to allocate resources to computationally expensive codes on the fly, enabling StructOpt to take full advantage of the parallelization tools available in many scientific packages. Graphical abstract Download : Download high-res image (86KB) Download : Download full-size image
KeyWords AITranslate
Basic Information:
DOI:https://doi.org/10.1016/j.commatsci.2018.12.052
Chinese Library Classification Number:
Citation Information:
Highlights • StructOpt is an atomic optimization software package that uses genetic algorithms. • StructOpt determines atomic structures using multiobjective optimization. • StructOpt focuses on electron microscopy experimental data and simulated energies. • StructOpt can integrate already-existing experimental data simulations. • StructOpt’s code is open source and available on GitHub. StructOpt, an open-source structure optimization suite, applies genetic algorithm and particle swarm methods to obtain atomic structures that minimize an objective function. The objective function typically consists of the energy and the error between simulated and experimental data, which is typically applied to determine structures that minimize energy to the extent possible while also being fully consistent with available experimental data. We present example use cases including the structure of a metastable Pt nanoparticle determined from energetic and scanning transmission electron microscopy data, and the structure of an amorphous-nanocrystal composite determined from energetic and fluctuation electron microscopy data. StructOpt is modular in its construction and therefore is naturally extensible to include new materials simulation modules or new optimization methods, either written by the user or existing in other code packages. It uses the Message Passing Interface’s (MPI) dynamic process management functionality to allocate resources to computationally expensive codes on the fly, enabling StructOpt to take full advantage of the parallelization tools available in many scientific packages. Graphical abstract Download : Download high-res image (86KB) Download : Download full-size image
quote
| GB/T 7714-2015 | [1] Jason J. Maldonis, Zhongnan Xu, Zhewen Song, et al. Computational Materials Science, 2019(160). DOI:10.1016/j.commatsci.2018.12.052. |
| MLA | [1] Jason J. Maldonis, et al., Computational Materials Science, no. 160, 2019, https://doi.org/10.1016/j.commatsci.2018.12.052. |
| APA | [1] Jason J. Maldonis, Zhongnan Xu, Zhewen Song, Min Yu, Tam Mayeshiba, Dane Morgan, & Paul M. Voyles. (2019). Computational Materials Science(160). https://doi.org/10.1016/j.commatsci.2018.12.052 |
| IEEE | [1] Jason J. Maldonis, Zhongnan Xu, Zhewen Song, Min Yu, Tam Mayeshiba, Dane Morgan, and Paul M. Voyles, Computational Materials Science, no. 160, 2019, doi: 10.1016/j.commatsci.2018.12.052. |
