  Machine learning techniques are attractive options for developing
highly-accurate automated analysis tools for nanomaterials characterization,
including high-resolution transmission electron microscopy (HRTEM). However,
successfully implementing such machine learning tools can be difficult due to
the challenges in procuring sufficiently large, high-quality training datasets
from experiments. In this work, we introduce Construction Zone, a Python
package for rapidly generating complex nanoscale atomic structures, and develop
an end-to-end workflow for creating large simulated databases for training
neural networks. Construction Zone enables fast, systematic sampling of
realistic nanomaterial structures, and can be used as a random structure
generator for simulated databases, which is important for generating large,
diverse synthetic datasets. Using HRTEM imaging as an example, we train a
series of neural networks on various subsets of our simulated databases to
segment nanoparticles and holistically study the data curation process to
understand how various aspects of the curated simulated data -- including
simulation fidelity, the distribution of atomic structures, and the
distribution of imaging conditions -- affect model performance across several
experimental benchmarks. Using our results, we are able to achieve
state-of-the-art segmentation performance on experimental HRTEM images of
nanoparticles from several experimental benchmarks and, further, we discuss
robust strategies for consistently achieving high performance with machine
learning in experimental settings using purely synthetic data.
