Atomic-level modeling performed at large scales enables the investigation of
mesoscale materials properties with atom-by-atom resolution. The spatial
complexity of such cross-scale simulations renders them unsuitable for simple
human visual inspection. Instead, specialized structure characterization
techniques are required to aid interpretation. These have historically been
challenging to construct, requiring significant intuition and effort. Here we
propose an alternative framework for a fundamental structural characterization
task: classifying atoms according to the crystal structure to which they
belong. Our approach is data-centric and favors the employment of Machine
Learning over heuristic rules of classification. A group of data-science tools
and simple local descriptors of atomic structure are employed together with an
efficient synthetic training set. We also introduce the first standard and
publicly available benchmark data set for evaluation of algorithms for
crystal-structure classification. It is demonstrated that our data-centric
framework outperforms all of the most popular heuristic methods -- especially
at high temperatures when lattices are the most distorted -- while introducing
a systematic route for generalization to new crystal structures. Moreover,
through the use of outlier detection algorithms our approach is capable of
discerning between amorphous atomic motifs (i.e., noncrystalline phases) and
unknown crystal structures, making it uniquely suited for exploratory materials
synthesis simulations.Comment: 16 pages, 7 figure
