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Abstract Nuclear quantum effects (NQEs) influence many physical and chemical phenomena, particularly those involving light atoms or occurring at low temperatures. However, their impact has been carefully quantified in few systems-like water-and is rarely considered more broadly. Here we use path-integral molecular dynamics to systematically investigate NQEs on thermophysical properties of 92 organic liquids at ambient conditions. Depending on chemical constitution, we find substantial impact across thermal expansivity, compressibility, dielectric constant, enthalpy of vaporization, and notably molar volume, which shows consistent, positive quantum-classical differences up to 5%; similar, less pronounced trends manifest as isotope effects from deuteration. Using data-driven analysis, we identify three features-molar mass, classical hydrogen density, and classical thermal expansivity-that accurately predict NQEs and facilitate understanding of how characteristics like branching and heteroatom content influence behavior. This work highlights the broad relevance of NQEs in molecular liquids, while also providing a conceptual and practical framework to anticipate their impact. 

Cononsolvency occurs when mixing two good solvents results in poor-solvent conditions for polymers. We examine how orientation-dependent interactions influence microscopic mechanisms of cononsolvency. 

Single-chain nanoparticles (SCNPs) formviaintramolecular folding and cross-linking of polymers. We examine how dimensionless design parameters dictate SCNP morphology, highlighting the influence of shear flow. 

A water-soluble molecular cage enables selective, solvent-free recovery and regeneration of PFAAs, offering a scalable, sustainable solution. 

Single-chain nanoparticles (SCNPs) are a class of materials formed by the intramolecular cross-linking and collapse of single polymer chains. Because their morphology dictates suitability for specific applications, such as nanoscale reactors and drug delivery vehicles, understanding how to control or tailor morphologies is of interest. Here, we investigate how the morphology of SCNPs depends on both precursor chain attributes, such as linker fraction and backbone stiffness, and an imposed shear flow. Using coarse-grained molecular dynamics simulations, we generate an ensemble of structures from 10,800 unique SCNPs, some formed under quiescent conditions and some in shear flow--the latter of which has not been previously studied. We then characterize morphologies by analysis of a three-dimensional embedding space obtained through unsupervised learning of the simulated structures. This reveals how SCNP morphology depends on dimensionless parameters, related to precursor-chain attributes and shear rate, and offers insight into their relative influence. Interestingly, we find that shear rate has comparable influence to the degree of polymerization and the blockiness of reactive sites. Furthermore, shear, which can be externally controlled independent of precursor chain synthesis, can have persistent effects on morphology, such as enhancing compaction of SCNPs based on stiff chains. This work provides guidelines for designing SCNPs with targeted characteristics based on five dimensionless variables and illustrates the utility of machine learning in analyzing SCNPs formed across a range of conditions. 

We benchmark the performance of space-filling and active learning algorithms on classification problems in materials science, revealing trends in optimally data-efficient algorithms. 

{"Abstract":["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.\n\nReferencesThe 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"\n\nDataThere are three .zip files that contain HOOMD-blue GSD trajectory files.\n\n\n\nconformations_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).\n\nsampled_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.\n\nsampled_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.\n\n\nUsageTo write the data in a .zip file into a pandas dataframe, users can execute the following: \n\nimport zipfile\nfrom pathlib import Path\nimport tempfile\n\nimport freud\nimport gsd.hoomd\nimport numpy as np\nimport pandas as pd\n\nDATA_DIR = Path("your/custom/dir")\nZIP_NAME = "sampled_production_conformations.zip"\n\nrecords = []\n\n# Extract to a temporary directory\nwith zipfile.ZipFile(DATA_DIR / ZIP_NAME) as zf, tempfile.TemporaryDirectory() as tmpdir:\n zf.extractall(tmpdir)\n for gsd_path in Path(tmpdir).rglob("*.gsd"):\n # Pull backbone length from the filename; only backbone beads are considered in the analysis\n name = gsd_path.name\n n_backbone = int(name.split("n_backbone=")[1].split("_")[0])\n\n with gsd.hoomd.open(str(gsd_path), "r") as trajectory:\n for frame_index, frame in enumerate(trajectory):\n hoomd_box = freud.box.Box.from_box(frame.configuration.box)\n\n # Restrict to backbone particles and unwrap periodic boundary conditions\n positions = np.asarray(frame.particles.position)[:n_backbone]\n images = np.asarray(frame.particles.image)[:n_backbone]\n positions = hoomd_box.unwrap(positions, images)\n\n records.append(\n {\n "filename": name,\n "frame_index": frame_index,\n "n_backbone": n_backbone,\n "positions": positions,\n }\n )\n\n# Combine all frames into a single DataFrame ready for analysis\ndf = pd.DataFrame(records)\nprint(df.head())\n\nHelp, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Matthew Chertok at matthewchertok@princeton.edu\n\nGitHubAdditional data and code relevant for this study is accessible at https://github.com/webbtheosim/md-simulation-files/tree/main/2025-scnp-shear"]}