SCNP-Shearflow-10k: dataset of single-chain nanoparticle morphologies under diverse shear conditions from 10,800 coarse-grained molecular dynamics simulations

This record provides access to 10,800 initial configurations and 216,000 morphological snapshots of cross-linked single-chain nanoparticles (SCNPs). Of the 108,000 production snapshots, 81,000 correspond to SCNPs cross-linked under shear (across three distinct shear rates) and 27,000 correspond to SCNPs cross-linked under quiescent conditions. The remaining 108,000 snapshots capture the same SCNPs after quiescent relaxation. Data are organized into three ZIP archives, which extract to HOOMD-blue GSD trajectory files. The dataset was compiled using Python 3.12.3. ReferencesThe specific applications and analyses of this dataset are described in1. Chertok, M.D.; Stone, H.A.; Webb, M.A. "Effect of Shear Flow and Precursor Polymer Design on Single-Chain Nanoparticle Formation" DataThere are three .zip files that contain HOOMD-blue GSD trajectory files. conformations_during_init_n=50_100_150.zip: contains the GSD files used for initial configurations. There are 1,080 files in total, each with 10 frames. Each frame represents one replicate (an independent simulation of the same system from a different initial configuration under identical conditions). sampled_production_conformations.zip: contains 10,800 GSD files. Each file includes the 10 snapshots used to analyze SCNP morphologies under shear. Snapshots were sampled uniformly from the final 50% of trajectory frames, after cross-linking was complete while shear remained applied. sampled_post_crosslinking_eq_conformations.zip: contains 10,800 GSD files. Each file includes the 10 snapshots used to analyze SCNP morphologies after quiescent relaxation. Snapshots were sampled uniformly from the final 83.3% of frames, after shear was removed and chains had sufficient time to relax. UsageTo write the data in a .zip file into a pandas dataframe, users can execute the following: import zipfile from pathlib import Path import tempfile import freud import gsd.hoomd import numpy as np import pandas as pd DATA_DIR = Path("your/custom/dir") ZIP_NAME = "sampled_production_conformations.zip" records = [] # Extract to a temporary directory with zipfile.ZipFile(DATA_DIR / ZIP_NAME) as zf, tempfile.TemporaryDirectory() as tmpdir: zf.extractall(tmpdir) for gsd_path in Path(tmpdir).rglob("*.gsd"): # Pull backbone length from the filename; only backbone beads are considered in the analysis name = gsd_path.name n_backbone = int(name.split("n_backbone=")[1].split("_")[0]) with gsd.hoomd.open(str(gsd_path), "r") as trajectory: for frame_index, frame in enumerate(trajectory): hoomd_box = freud.box.Box.from_box(frame.configuration.box) # Restrict to backbone particles and unwrap periodic boundary conditions positions = np.asarray(frame.particles.position)[:n_backbone] images = np.asarray(frame.particles.image)[:n_backbone] positions = hoomd_box.unwrap(positions, images) records.append( { "filename": name, "frame_index": frame_index, "n_backbone": n_backbone, "positions": positions, } ) # Combine all frames into a single DataFrame ready for analysis df = pd.DataFrame(records) print(df.head()) Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Matthew Chertok at [e-mail] GitHubAdditional data and code relevant for this study is accessible at https://github.com/webbtheosim/md-simulation-files/tree/main/2025-scnp-shear