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1. ToPoRg-18k: dataset of single-chain radii of gyration distribution for 18,450 architecturally diverse and chemically patterned coarse-grained polymers

   https://doi.org/10.5281/ZENODO.13619700  

   Jiang, Shengli; Michael_A, Webb (January 2025, Zenodo)

   Revision: This revision includes four independent trajectory values of the ensemble averages of the mean squared radii of gyration and their standard deviations, which can be used to compute statistical measures such as the standard error. This distribution provides access to 18,450 configurations of coarse-grained polymers. The data is provided as a serialized object using the `pickle' Python module and in csv format. The data was compiled using Python version 3.8.  ReferencesThe specific applications and analyses of the data are described in 1.  Jiang, S.; Webb, M.A. "Physics-Guided Neural Networks for Transferable Prediction of Polymer Properties" DataThere are seven .pickle files that contain serialized Python objects. pattern_graph_data_*_*_rg_new.pickle: squared radii of gyration distribution from MD simulation. The number indicates the molecular weight range. rg2_baseline_*_new.pickle: squared radii of gyration distribution from Gaussian chain theoretical prediction. delta_data_v0314.pickle: torch_geometric training data. UsageTo access the data in the .pickle file, users can execute the following: # LOAD SIMULATION DATADATA_DIR = "your/custom/dir/"mw = 40 # or 90, 190 MWs filename = os.path.join(DATA_DIR, f"pattern_graph_data_{mw}_{mw+20}_rg_new.pickle")with open(filename, "rb") as handle:    graph = pickle.load(handle)    label = pickle.load(handle)    desc  = pickle.load(handle)    meta  = pickle.load(handle)    mode  = pickle.load(handle)    rg2_mean   = pickle.load(handle)    rg2_std    = pickle.load(handle) ** 0.5 # var # combine asymmetric and symmetric star polymerslabel[label == 'stara'] = 'star'# combine bottlebrush and other comb polymerslabel[label == 'bottlebrush'] = 'comb'  # LOAD GAUSSIAN CHAIN THEORETICAL DATAwith open(os.path.join(DATA_DIR, f"rg2_baseline_{mw}_new.pickle"), "rb") as handle:    rg2_mean_theo = pickle.load(handle)[:, 0]    rg2_std_theo = pickle.load(handle)[:, 0] graph: NetworkX graph representations of polymers. label: Architectural classes of polymers (e.g., linear, cyclic, star, branch, comb, dendrimer). desc: Topological descriptors (optional). meta: Identifiers for unique architectures (optional). mode: Identifiers for unique chemical patterns (optional). rg2_mean: Mean squared radii of gyration from simulations. rg2_std: Corresponding standard deviation from simulations. rg2_mean_theo: Mean squared radii of gyration from theoretical models. rg2_std_theo: Corresponding standard deviation from theoretical models. Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/gcgnn 

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2. TopoSolv-6k: Radii of Gyration and Shear-Rate Dependent Viscosities for Topologically and Chemically Diverse Coarse-Grained Polymers

   https://doi.org/10.5281/ZENODO.16696725  

   Jiang, Shengli; Webb, Michael (January 2025, Zenodo)

   Dataset Description This dataset contains 6710 structural configurations and solvophobicity values for topologically and chemically diverse coarse-grained polymer chains. Additionally, 480 polymers include shear-rate dependent viscosity profiles at 2 wt% polymer concentration.The data is provided as serialized objects using the pickle Python module.All files were generated using Python version 3.10. Data There are three pickle files containing serialized Python objects. Key files include: data_aug10.pickle  Contains the coarse-grained polymer dataset with 6710 entries.  Each entry includes: Polymer graph Squared radius of gyration (at lambda = 0). Solvophobicity (lambda). Bead count (N). Chain virial number (Xi). topo_param_visc.pickle   Shear-rate-dependent viscosity profiles of 480 polymer systems. target_curves.pickle  Contains 30 target viscosity profiles used for active learning. Usage To load the dataset stored in data_aug10.pickle, use the following code: import pickle with open("data_aug10.pickle", "rb") as handle:    (        (x_train, y_train, c_train, l_train, graph_train),        (x_valid, y_valid, c_valid, l_valid, graph_valid),        (x_test, y_test, c_test, l_test, graph_test),        NAMES,        SCALER,        SCALER_y,        le    ) = pickle.load(handle) x: node features for each polymer graph y: labels (e.g., predicted properties) c: topological class indices l: topological descriptors graph: NetworkX graphs representing polymer topology NAMES: list of topological class names SCALER: fitted scaler for topological descriptors (l) SCALER_y: fitted scaler for property labels (y) le: label encoder for topological class indices   To load the dataset stored in topo_param_visc.pickle, use the following code: import pickle with open("poly_data_ml.pickle", "rb") as handle:    desc_all, ps_all, curve_all, shear_rate, graph_all = pickle.load(handle) desc_all: topological descriptors for each polymer graph ps_all: fitted Carreau–Yasuda model parameters curve_all: fitted viscosity curves shear_rate: shear rates corresponding to each viscosity curve graph_all: polymer graphs represented as NetworkX objects   First 30: seed dataset Next 150: 5 iterations (30 each) from class-balanced space-filling Following 150: space-filling without class balancing Final 150: active learning samples    To load the dataset stored in target_curves.pickle, use the following code: import pickle with open("target_curves.pickle", "rb") as handle:    data = pickle.load(f) curves = data['curves']params = data['params']shear_rate = data["xx"]   curves: target viscosity curves used as design objectives params: Carreau–Yasuda model parameters fitted to the target curves shear_rate: shear rate values associated with the target curves     Help, Suggestions, Corrections?If you need help, have suggestions, identify issues, or have corrections, please send your comments to Shengli Jiang at sj0161@princeton.edu GitHubAdditional data and code relevant for this study is additionally accessible at https://github.com/webbtheosim/cg-topo-solv 

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

   https://doi.org/10.5281/zenodo.17203738  

   Chertok, Matthew; Stone, Howard; Webb, Michael (January 2025, Zenodo)

   {"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"]} 

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4. Thermal stability and phase transformation of α-, κ(ε)-, and γ-Ga2O3 films under different ambient conditions

   https://doi.org/10.1063/5.0214500  

   Tang, Jingyu; Jiang, Kunyao; Tseng, Po-Sen; Kurchin, Rachel C; Porter, Lisa M; Davis, Robert F (August 2024, Applied Physics Letters)

   Phase transitions in metastable α-, κ(ε)-, and γ-Ga2O3 films to thermodynamically stable β-Ga2O3 during annealing in air, N2, and vacuum have been systematically investigated via in situ high-temperature x-ray diffraction (HT-XRD) and scanning electron microscopy (SEM). These respective polymorphs exhibited thermal stability to ∼471–525 °C, ∼773–825 °C, and ∼490–575 °C before transforming into β-Ga2O3, across all tested ambient conditions. Particular crystallographic orientation relationships were observed before and after the phase transitions, i.e., (0001) α-Ga2O3 → (2¯01) β-Ga2O3, (001) κ(ε)-Ga2O3 → (310) and (2¯01) β-Ga2O3, and (100) γ-Ga2O3 → (100) β-Ga2O3. The phase transition of α-Ga2O3 to β-Ga2O3 resulted in catastrophic damage to the film and upheaval of the surface. The respective primary and possibly secondary causes of this damage are the +8.6% volume expansion and the dual displacive and reconstructive transformations that occur during this transition. The κ(ε)- and γ-Ga2O3 films converted to β-Ga2O3 via singular reconstructive transformations with small changes in volume and unchanged surface microstructures. 

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5. Data from: Advancing the Ca14AlSb11 structure type: Synthesis and characterization of Yb14CdSb11 for thermoelectric applications

   https://doi.org/10.5061/dryad.9w0vt4brw  

   Kauzlarich, Susan; Opara, Ivan (January 2025, Dryad)

   Abstract: The ternary phase Yb14CdSb11 has been synthesized using both flux and polycrystalline methods. The crystal structure was determined via single-crystal X-ray diffraction, revealing that it crystallizes in the Ca14AlSb11 structure type (I41/acd space group with unit cell parameters of a = 16.5962(2) Å and c = 22.1346(5) Å, 90 K, Z = 8, R1 = 2.65 %: wR2 = 4.58 %. The polycrystalline form of the compound was synthesized from a stoichiometric reaction of Yb4Sb3, CdSb, Yb, and Sb. The elemental composition was confirmed using scanning electron microscopy and energy-dispersive spectroscopy (SEM-EDS), and phase purity was verified by powder X-ray diffraction (PXRD). Thermoelectric measurements, including resistivity, Seebeck coefficient, thermal conductivity, Hall carrier concentration, and Hall mobility, were conducted from 300 to 1273 K. Yb14CdSb11 exhibited a peak zT = 0.80 at 1150 K. Carrier concentration and Hall mobility ranged from to  holescm^-3^ and from 1019 cm²V-¹s-^¹, respectively. This carrier concentration is lower than that reported for the Zn or Mn analogs, leading to a lower thermoelectric figure of merit at high temperatures. However, with appropriate doping, it is expected that this phase should also be a promising p-type candidate for high-temperature energy conversion applications. TechnicalInfo: # Data from: Advancing the Ca14AlSb11 structure type: Synthesis and characterization of Yb14CdSb11 for thermoelectric applications ## Description of the data and file structure ### Files and variables #### File: Dryad_PXRD_data.zip **Description:**  Heat capacity, powder x-ray diffraction data for CdSb, Yb4Sb3, and Yb14CdSb11, carrier concentration, mobility, electrical resistivity, Seebeck coefficients, thermal conductivity, and zT for Yb14CdSb11. The zip file entitled Yb14CdSb11_All_Data.zip includes data files with Heat Capacity ("Heat capacity_YbCdSb.dat"), Powder X-ray Diffraction data of the binary precursors ("PXRD CdSb.txt", "PXRD Yb4Sb3.txt", and "PXRD_Yb14CdSb11.txt", and Thermoelectric transport data. **Heat Capacity** Heat capacity measurements of a single crystal were done in a Quantum Design PPMS from 2 K to 300 K. **Powder X-ray Diffraction (PXRD)** PXRD data were collected on each sample using a Bruker zero background holder on a Bruker D8 Eco Advanced employing Cu K∝ radiation (λ = 1.54 Å) with a Ni filter to remove Cu Kβ. **Seebeck Coefficient and Electrical Resistivity** Seebeck, resistivity, and Hall data were collected as a function of temperature under high vacuum at the Jet Propulsion Laboratory (JPL). Resistivity and Hall Van der Pauw measurements were performed with a current of 100 mA and a 0.8 T magnet. Seebeck coefficient was measured using a custom two-probe instrument under a high vacuum. Temperature-dependent Seebeck coefficients, electrical resistivity, and thermal conductivity were fit to six-order polynomials to calculate* zT* values. T (K):** **Temperature in Kelvin; r-fit (mΩ·cm): electrical resistivity; S-fit (µV/K): Seebeck coefficient; L- Lorenz number; K-fit: total thermal conductivity; K_e: electronic thermal conductivity; K_L: lattice (phonon) thermal conductivity; PF: power factor; ZT (dimensionless): thermoelectric figure of merit. **Thermal Conductivity** Thermal diffusivity was measured on densified pellets using laser flash analysis on a Netzsch LFA 475 Microflash under Ar(g) flow. The fully densified pellet was sliced into a thin disk (<1.5 mm) and polished until flat with parallel sides. The density of this disk was measured using Archimedes' method with toluene as the liquid. The heat capacity of Yb14CdSb11 was estimated using an adjusted heat capacity, Cp, based on the experimental heat capacity for Yb14MnSb11 and adjusting for the molecular weight of the Yb14CdSb11 analog: Where MM is the molar mass of the respective compounds. This adjusted heat capacity was employed to give the total thermal conductivity according to the equation: κ = D × Cp × *d* (D = measured diffusivity; Cp = heat capacity adjusted; *d* = the temperature-adjusted density).  The coefficient of thermal expansion (CTE) measured for Yb14MnSb11 was also employed. This procedure is consistent with past practice for compounds with the Yb14MSb11, compared to previously published work. 

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