TY - JOUR
T1 - Multi-atom pattern analysis for binary superlattices
AU - Reinhart, Wesley F.
AU - Panagiotopoulos, Athanassios Z.
N1 - Funding Information:
We thank Hasan Zerze (Lehigh University), Jeetain Mittal (Lehigh University), and Nathan Mahynski (NIST) for providing snapshots from their simulations, and Andrew Long (UIUC Urbana-Champaign) for helpful discussions. Research for this publication was conducted with government support under contract FA950-11-C-0028 and awarded by the Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. Additional support was provided by the U.S. National Science Foundation under award CBET-1402166.
Publisher Copyright:
© The Royal Society of Chemistry 2017.
PY - 2017
Y1 - 2017
N2 - We present a method for the template-free characterization of binary superlattices. This is an extension of the Neighborhood Graph Analysis method, a technique which evaluates relationships between observed structures based on the topology of their first coordination shell [W. F. Reinhart, et al., Soft Matter, 2017, 13, 4733]. In the present work, we develop a framework for the analysis of multi-atom patterns, which incorporate structural information from the second coordination shell while providing a unified signature for all constituent particles in the superlattice. We construct an efficient metric for making quantitative comparisons between these patterns, making our algorithm the first capable of characterizing partial or defective superlattice structures. As in our previous work, we leverage machine learning techniques to characterize a range of self-assembled crystal structures, discovering a set of emergent collective variables which map each observed pattern into an intuitive global phase space. We demonstrate the method by performing classification of configurations from simulations of binary colloidal self-assembly in two dimensions.
AB - We present a method for the template-free characterization of binary superlattices. This is an extension of the Neighborhood Graph Analysis method, a technique which evaluates relationships between observed structures based on the topology of their first coordination shell [W. F. Reinhart, et al., Soft Matter, 2017, 13, 4733]. In the present work, we develop a framework for the analysis of multi-atom patterns, which incorporate structural information from the second coordination shell while providing a unified signature for all constituent particles in the superlattice. We construct an efficient metric for making quantitative comparisons between these patterns, making our algorithm the first capable of characterizing partial or defective superlattice structures. As in our previous work, we leverage machine learning techniques to characterize a range of self-assembled crystal structures, discovering a set of emergent collective variables which map each observed pattern into an intuitive global phase space. We demonstrate the method by performing classification of configurations from simulations of binary colloidal self-assembly in two dimensions.
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U2 - 10.1039/c7sm01642e
DO - 10.1039/c7sm01642e
M3 - Article
C2 - 28949366
AN - SCOPUS:85030774217
SN - 1744-683X
VL - 13
SP - 6803
EP - 6809
JO - Soft matter
JF - Soft matter
IS - 38
ER -