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Title: Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation

Abstract

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases. Published by AIP Publishing.

Authors:
 [1];  [2];  [3]; ORCiD logo [4];  [5]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [6]
  1. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; Google, Inc., Mountain View, California 94030, USA
  2. Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  3. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  4. Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universiät München, Lichtenbergstr. 4, D-85747 Garching, Germany
  5. Argonne Leadership Computing Facility, Argonne National Lab, Lemont, Illinois 60439, USA
  6. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Argonne National Laboratory - Argonne Leadership Computing Facility; National Science Foundation (NSF)
OSTI Identifier:
1465530
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 148; Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English

Citation Formats

Li, Xiayue, Curtis, Farren S., Rose, Timothy, Schober, Christoph, Vazquez-Mayagoitia, Alvaro, Reuter, Karsten, Oberhofer, Harald, and Marom, Noa. Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation. United States: N. p., 2018. Web. doi:10.1063/1.5014038.
Li, Xiayue, Curtis, Farren S., Rose, Timothy, Schober, Christoph, Vazquez-Mayagoitia, Alvaro, Reuter, Karsten, Oberhofer, Harald, & Marom, Noa. Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation. United States. doi:10.1063/1.5014038.
Li, Xiayue, Curtis, Farren S., Rose, Timothy, Schober, Christoph, Vazquez-Mayagoitia, Alvaro, Reuter, Karsten, Oberhofer, Harald, and Marom, Noa. Thu . "Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation". United States. doi:10.1063/1.5014038.
@article{osti_1465530,
title = {Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation},
author = {Li, Xiayue and Curtis, Farren S. and Rose, Timothy and Schober, Christoph and Vazquez-Mayagoitia, Alvaro and Reuter, Karsten and Oberhofer, Harald and Marom, Noa},
abstractNote = {We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases. Published by AIP Publishing.},
doi = {10.1063/1.5014038},
journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = 24,
volume = 148,
place = {United States},
year = {2018},
month = {6}
}

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