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Title: GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction

Abstract

We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. Furthermore, the experimentally observed structures and other low-energy structures are foundmore » for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with PT symmetry and a scaffold packing motif, which has not been reported previously.« less

Authors:
 [1];  [2];  [1];  [3];  [4];  [5]; ORCiD logo [1]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  2. Google, Mountain View, CA (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
  3. Argonne National Lab. (ANL), Lemont, IL (United States)
  4. Indian Institute of Technology Delhi, New Delhi (India)
  5. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin (Germany)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF); Argonne National Laboratory, Argonne Leadership Computing Facility; USDOE
OSTI Identifier:
1461431
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 14; Journal Issue: 4; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Curtis, Farren, Li, Xiayue, Rose, Timothy, Vázquez-Mayagoitia, Álvaro, Bhattacharya, Saswata, Ghiringhelli, Luca M., and Marom, Noa. GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction. United States: N. p., 2018. Web. doi:10.1021/acs.jctc.7b01152.
Curtis, Farren, Li, Xiayue, Rose, Timothy, Vázquez-Mayagoitia, Álvaro, Bhattacharya, Saswata, Ghiringhelli, Luca M., & Marom, Noa. GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction. United States. doi:10.1021/acs.jctc.7b01152.
Curtis, Farren, Li, Xiayue, Rose, Timothy, Vázquez-Mayagoitia, Álvaro, Bhattacharya, Saswata, Ghiringhelli, Luca M., and Marom, Noa. Mon . "GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction". United States. doi:10.1021/acs.jctc.7b01152. https://www.osti.gov/servlets/purl/1461431.
@article{osti_1461431,
title = {GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction},
author = {Curtis, Farren and Li, Xiayue and Rose, Timothy and Vázquez-Mayagoitia, Álvaro and Bhattacharya, Saswata and Ghiringhelli, Luca M. and Marom, Noa},
abstractNote = {We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. Furthermore, the experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with PT symmetry and a scaffold packing motif, which has not been reported previously.},
doi = {10.1021/acs.jctc.7b01152},
journal = {Journal of Chemical Theory and Computation},
issn = {1549-9618},
number = 4,
volume = 14,
place = {United States},
year = {2018},
month = {2}
}

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