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81 to 90 of 2,784 Results
yb2si2o7_eq_vol-ph.016.out
May 29, 2026 - Replication Data for: The infrared optical properties of ytterbium disilicate
Unknown - 1.4 MB - MD5: b263d8696958f68b74bc499a6ee35c7c
yb2si2o7_eq_vol-ph.017.out
May 29, 2026 - Replication Data for: The infrared optical properties of ytterbium disilicate
Unknown - 1.4 MB - MD5: 1dfdc0ebba0efe6a5b678c63332da441
yb2si2o7_eq_vol-ph.018.out
May 29, 2026 - Replication Data for: The infrared optical properties of ytterbium disilicate
Unknown - 1.1 MB - MD5: ee0773981e352b8ce49b2efca3ef9f52
yb2si2o7_eq_vol-ph.019.out
May 29, 2026 - Replication Data for: The infrared optical properties of ytterbium disilicate
Unknown - 1.1 MB - MD5: 0572b2d9011860d5287632156f4d8f9a
yb2si2o7_eq_vol-ph.in
May 29, 2026 - Replication Data for: The infrared optical properties of ytterbium disilicate
Unknown - 2.3 KB - MD5: 3ce8a2bdf32e0db67d5a2d487021bead
Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys
Mar 30, 2026 - Materials Informatics Group
Garg, Sunidhi; Jishnu Bhattacharyya; Vineet V. Joshi; Sean R. Agnew; Prasanna V. Balachandran, 2026, "Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys", https://doi.org/10.18130/V3/EXJA3W, University of Virginia Dataverse, V1
The data in each sheet is described below: 1. Data_train: Training dataset used for all the ML models 2. Data_test: Test dataset used for all the ML models 3. Data_virtual: Dataset having the principal component (PC) values of U-Mo-X alloys absent from train and test set and is the input to predict the TTT curves for these U-Mo-X alloys 4. Data_cal...
README.txt
Mar 30, 2026 - Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys
Plain Text - 823 B - MD5: b6803b838e30cb5216986301298e1f50
SI_dataset.zip
Mar 30, 2026 - Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys
ZIP Archive - 164.4 KB - MD5: 37917b1d4b30e53c3c6bad50ae51db26
Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys
Mar 23, 2026 - Materials Informatics Group
Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys", https://doi.org/10.18130/V3/AD6H08, University of Virginia Dataverse, V1
Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys. This repository contains relaxation trajectories for all BCC HEA crystal structures, and 11 template SQS structures.
Supplemental_Materials.zip
Mar 23, 2026 - Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys
ZIP Archive - 154.9 MB - MD5: c9599f1e6aaffb1ff219e6e8bc438233
All trajectory data, 11 SQS structure templates, and eSVR prediction for all 126 composition
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