361 Works

The Effect of Polymer Grafting on the Mechanical Properties of PEG-grafted Cellulose Nanocrystals in Poly(Lactic Acid)

Nicholas Macke, Christina M. Hemminsen & Stuart J. Rowan
Contains data for the publication "The Effect of Polymer Grafting on the Mechanical Properties of PEG-grafted Cellulose Nanocrystals in Poly(Lactic Acid)". Includes AFM, conductivity, DMA, DSC, Kaiser testing, tensile testing, TGA, and WAXS data along with source files for the manuscript figures.

Dataset for \"The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations\"

Amanda Dumi, Shiv Upadhyay, Leonardo Bernasconi, Hyeondeok Shin, Anouar Benali & Kenneth D. Jordan
The files and data used for "The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations". A.B. and H.S were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials. An award of computer time was provided by the Innovative and Novel Computational Impact on...

Ultrasensitive Molecular Sensors Based on Real‐Time Impedance Spectroscopy in Solution‐Processed 2D Materials

David C. Moore, Ali Jawaid, Robert Busch, Michael Brothers, Paige Miesle, Adam Miesle, Rahul Rao, Jonghoon Lee, Lucas K. Beagle, Michael Motala, Shay Goff Wallace, Julia R. Downing, Ajit Roy, Christopher Muratore, Mark C. Hersam, Richard Vaia, Steve Kim & Nicholas R. Glavin
Chemical sensors based on solution-processed 2D nanomaterials represent an extremely attractive approach toward scalable and low-cost devices. Through the implementation of real-time impedance spectroscopy and development of a three-element circuit model, redox exfoliated MoS2 nanoflakes demonstrate an ultrasensitive empirical detection limit of NO2 gas at 1 ppb, with an extrapolated ultimate detection limit approaching 63 ppt. This sensor construct reveals a more than three orders of magnitude improvement from conventional direct current sensing approaches as...

GAP-ML model, training and MD dataset for \"A Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by HEXRD\"

Jicheng Guo, Logan Ward, Yadu Babuji, Nathaniel Hoyt, Mark Williamson, Ian Foster, Nicholas Jackson, Chris Benmore & Ganesh Sivaraman
## Usage Notes ### ML training dataset and potential 1) "POT/" ### MD 1. The full MD trajectory for the thermal conducitivity calculations can be found in "MD_WAVE_METHOD/" 2. All the MD trajectories used for structure factor / CN estimation at multiple composition / temperatures can be foudn in "MD/". The ".tar.gz" file with '.extxyz' trajectory is according to the LiCl-KCl molar fraction followed by the temperature in K units. 3. The "MD/" folder also...

GEOM with Hessians

Simon Axelrod & Rafael Gómez-Bombarelli
Here you can find 1.3 million Hessians from 1,511 species in the BACE dataset. The conformers were computed with CREST using xTB, and released as part of the GEOM dataset. If you use this data, please cite our paper: https://www.nature.com/articles/s41597-022-01288-4.

Supporting data for \"Temporally coherent backmapping of molecular trajectories from coarse-grain to atomistic resolution\"

Kirill Shmilovich, Marc Stieffenhofer, Nicholas E. Charron & Moritz Hoffmann
This repository contains supporting data and code for the paper titled "Temporally coherent backmapping of molecular trajectories from coarse-grain to atomistic resolution" by Kirill Shmilovich, Marc Stieffenhofer, Nicholas E. Charron, and Moritz Hoffmann

Exploring effective charge in electromigration using machine learning

Yu-chen Liu, Ben Afflerbach, Ryan Jacobs, Shih-kang Lin & Dane Morgan
Dataset containing effective charge values for 49 metal host-impurity pairs

Mechanical properties of some steels

Gareth Conduit
Dataset containing compositions and mechanical properties (yield strength, tensile strength, elongation) of 312 steel alloys

Data for Using 4D STEM to probe mesoscale order in molecular glass films prepared by physical vapor deposition

Debaditya Chatterjee, Shuoyuan Huang, Kaichen Gu, Jianzhu Ju, Junguang Yu, Harald Bock, Lian Yu, M. D. Ediger & Paul M. Voyles
4D STEM data for orientation mapping of phenanthroperylene ester anisotropic molecular glass thin films. Raw nanodiffraction data, intermediate analyses, and derived orientation maps for samples with varying domain size as a function of substrate temperature during deposition. Additional datasets from samples cooled for a higher temperature liquid crystal phase.

Scanning Electron Microscopy (SEM) images of Pathway-primed Layered Block Copolymer Thin Films

Kevin G. Yager & Sebastian T. Russell
Scanning electron microscopy (SEM) images (top-view and perspective view) of a variety of block copolymer (BCP) thin films. The films were formed by layering different BCP types and thermally annealing (at various temperatures and annealing times). The SEM images provide information about the nanostructure that forms as a result of the particular preparation/annealing history.

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Maciej P. Polak, Ryan Jacobs, Arun Mannodi-Kanakkithodi, Maria K. Y. Chan & Dane Morgan
Dataset containing DFT-calculated defect charge state transition levels of 2910 semiconductor-impurity pairs

BraggNN: Validation Dataset

Nikil Ravi, Zhengchun Liu, Hemant Sharma, Pranshu Chaturvedi, E.A. Huerta, Aristana Scourtas, Schmidt KJ, Ryan Chard & Ben Blaiszik
# BraggNN Validation Dataset This dataset contains `13799` samples from the training dataset, and is used to evaluate, and provide metrics for, the models that were trained on the larger training dataset. ## Data The data are located in the `/data` folder, and store the collected peaks and frames. There are two datasets in the hdf5 file: - `Patch`: input to the BraggNN model. It is a 3D array where its first dimension is the...

Autonomous TSAXS mapping of sDSA sample

Aaron Stein Gregory S. Doerk Marcus M. Noack, Masafumi Fukuto, Kevin G. Yager
Autonomous SAXS mapping of sDSA sample. Experimental transmission small-angle x-ray scattering (TSAXS) data collected as a function of position for a selective directed self-assembly (sDSA) combinatorial sample. Sample was a grid of lithographically-defined chemical patterns (varying pitch and dose) with a blend of block copolymer materials cast on top. Measurement positions were selected machine-learning algorithm implementing a Gaussian Process (GP) method. Dataset contains raw SAXS detector images, as well as analysis results converting the image...

A mechanics-based approach to realize high-force capacity electroadhesives for robots

David J. Levine, Gokulanand M. Iyer, Kevin T. Turner, James H. Pikul & R. Daelan Roosa
All data in this file accompanies the manuscript 'A mechanics-based approach to high-force capacity electroadhesives for robots', D.J. Levine, G.M. Iyer, R.D. Roosa, K.T. Turner & J.H. Pikul, 2022. All authors are associated with the University of Pennsylvania, Department of Mechanical Engineering & Applied Mechanics, Philadelphia, PA. All data in this file are represented in Figures 3 & 6 in the manuscript. All electroadhesive force capacity tests were run with clutch designs with a Parylene-C...

Orbital character of the spin-reorientation transition in TbMn6Sn6

Robert J McQueeney

FAIR Interaction Network Model for Higgs Boson Detection

Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, Maurizio Pierini, Ruike Zhu, Avik Roy & EA Huerta

Neutron diffraction structure factors of silicate glasses

Ying Shi, Jörg Neuefeind & Mathieu Bauchy

Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches

Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian & Chad Risko
Trained machine learning models on the [ocelot_chromophore_v1 dataset](https://acdc.alcf.anl.gov/mdf/detail/ocelot_chromophore_v1_v1.1/).

Sputter deposited Mo thin films: multimodal characterization of residual stress, resistivity, crystallinity, and surface morphology.

David P. Adams, Sadhvikas J. Addamane, Joyce O. Custer, Frank W. Delrio, Luis J. Jauregui, Matias S. Kalaswad, Ryan M. Khan & Amelia A. Henriksen
Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process-structure-property relationships and for designing materials with specific structures and/or properties. Here, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature applications, photovoltaics, and microelectronics. A process space consisting of 27 unique combinations of sputter power and Argon (Ar) deposition pressure was explored. The...

DefectTrack training dataset

Rajat Sainju, Wei-Ying Chen, Samuel Schaefer, Qian Yang, Caiwen Ding, Meimei Li & Yuanyuan Zhu

The Effects of Heat Treatment on Very High Cycle Fatigue Behavior in Hot-rolled WE43 Magnesium

Jacob F. Adams, John E. Allison & J. Wayne Jones
This is an experimental dataset testing integration between MDF and Materials Commons, underlying dataset may not be available immediately. This data can be access at the Materials Commons from the following link. ** Add Link **

Ostwald Ripening of Faceted Si Particles in an Al-Si-Cu Melt

Ashwin J. Shahani, Xianghui Xiao, Kwan Skinner, Matthew Peters & Peter W. Voorhees
This data was collected to study the isothermal coarsening of faceted Si particles in an Al-Si-Cu liquid. The as-cast, hyper-eutectic sample consisted of primary Si particles in a eutectic matrix. Upon heating to above the eutectic temperature (here, 650 C), the eutectic constituents melted and the Si particles were in contact with a featureless liquid. Tracking the evolution of the solid-liquid interfaces under isothermal conditions was the focus of this work. The raw data were...

Rotationally Commensurate Growth of MoS2 on Epitaxial Graphene

Xiaolong Liu, Itamar Balla, Hadallia Bergeron, Gavin J. Campbell, Michael J. Bedzyk & Mark C. Hersam
This data demonstrates the rotationally commensurate growth of atomically thin MoS2 on epitaxial graphene (EG) on silicon carbide using chemical vapor deposition. The characterization of the MoS2/EG heterostructure is performed using atomic force microscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, synchrotron X-ray scattering, atomic-resolution scanning tunneling microscopy (STM), and scanning tunneling spectroscopy. DOI: 10.1021/acsnano.5b06398

Liquid-solid Metallic Mixture Coarsening Data - 55% solid

John W. Gibbs, Peter W. Voorhees & Julie L. Fife
This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between...

Reciprocal Space Imaging of Ionic Correlations in Intercalation Compounds

Raymond Osborn, Matthew Krogstad, Stephan Rosenkranz, Justin Wozniak, Jacob Ruff, Vaughey John & Guy Jennings
These files contain data sets used in the 3D ΔPDF analysis of Na0.45V2O5, reported in “Reciprocal Space Imaging of Ionic Correlations in Intercalation Compounds” by Krogstad et al. that is being published in Nature Materials. For further information, contact Ray Osborn (rosborn@anl.gov). The files stored here are NeXus files (extension .nxs). These are HDF5 files that conform to the NeXus format (http://www.nexusformat.org). These may be opened in any generic HDF5 viewer, but it is easiest...

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