Data for Configurations and Characteristics of Simulated Single-Chain Nanoparticles

This item provides access to all configurations of single-chain
nanoparticles analyzed in the manuscript "Sequence Patterning,
Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from
Simulation and Machine Learning" by Roshan A. Patel, Sophia
Colmenares, and Michael A. Webb (DOI: 10.1021/acspolymersau.3c00007). The
single-chain nanoparticles derive from 320 unique precursor chains that
are distinguished by the fraction of linker beads that decorate a
fixed-length polymer backbone and the distribution or blockiness of those
linker beads. The data is provided in the form of serialized object using
the `pickle' python module. The data was compiled using Python
version 3.8.8 and Clang 10.0.0. The Python object loaded from the .pkl
file is a nested list, with the first dimension having 7,680 entries for
the 7,680 unique single-chain nanoparticles produced in the aforementioned
paper. Each of those 7,680 entries is itself a list with 20 entries,
representing the 20 different simulation snapshots of the given
single-chain nanoparticle. Each of the 20 entries is another list with two
entries, with the first being a numpy.ndarray containing the x,y,z
coordinates of all the beads comprising the single-chain nanoparticle and
the second being a numpy.ndarray with a numerical encoding to indicate
whether the beads are backbone (indicated as '0') or linker
beads (indicated as '1'). Altogether, this provides 153,600
configurations of single-chain nanoparticles.