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

Curtis, Farren; Li, Xiayue; Rose, Timothy; Vázquez-Mayagoitia, Álvaro; Bhattacharya, Saswata; Ghiringhelli, Luca M.; Marom, Noa

doi:10.1021/acs.jctc.7b01152

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

Journal Article · Mon Feb 26 00:00:00 EST 2018 · Journal of Chemical Theory and Computation

DOI:https://doi.org/10.1021/acs.jctc.7b01152· OSTI ID:1461431

Curtis, Farren ^[1]; Li, Xiayue ^[2]; Rose, Timothy ^[1]; Vázquez-Mayagoitia, Álvaro ^[3]; Bhattacharya, Saswata ^[4]; Ghiringhelli, Luca M. ^[5];

^[1]

Carnegie Mellon Univ., Pittsburgh, PA (United States)
Google, Mountain View, CA (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
Indian Institute of Technology Delhi, New Delhi (India)
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin (Germany)

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); Argonne National Laboratory, Argonne Leadership Computing Facility; USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1461431

Journal Information:: Journal of Chemical Theory and Computation, Vol. 14, Issue 4; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 59 works

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Bayesian inference of atomistic structure in functional materials Todorović, Milica; Gutmann, Michael U.; Corander, Jukka npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0175-2	journal	March 2019
Structure Prediction Drives Materials Discovery Oganov, Artem; Pickard, Christopher; Zhu, Qiang Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.39313	text	January 2019
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