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1 to 10 of 2,600 Results

Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys Mar 30, 2026 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 Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys Mar 23, 2026 Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Framework 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 Framework 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
Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia Feb 13, 2026 Liu, Shunshun; Clarke, David R.; Balachandran, Prasanna V., 2026, "Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia", https://doi.org/10.18130/V3/9DMDUS, University of Virginia Dataverse, V1 Collection of 16 transition metal-substituted YSZ (yttria-stabilized zirconia) structure files and corresponding Yambo GW-BSE output logs, organized by dopant and nominal charge state. The crystal structure file can be used to replicate the GW-BSE calculation discussed in the manuscript, and the GW-BSE output log can be used a reference of GW-BSE c...
README.md Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia Markdown Text - 1.3 KB - MD5: 5ca7084b17e4674b43dbaa1350aefa21
Co3YSZ.vasp Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia Unknown - 2.9 KB - MD5: a87a766be0c03968f946fe530bea0fa6
r-3D_BSE-diag_optics_dipoles_bss_bse Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia Unknown - 273.3 KB - MD5: ee13aa5649a066fad03b8f15b4060041
Cr3YSZ.vasp Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia Unknown - 2.9 KB - MD5: 0e2ec8368fcced483778676598049604

Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys

Mar 30, 2026

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 -

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 -

Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys

Mar 23, 2026

Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Framework 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 Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys

ZIP Archive - 154.9 MB -

All trajectory data, 11 SQS structure templates, and eSVR prediction for all 126 composition

Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia

Feb 13, 2026

Liu, Shunshun; Clarke, David R.; Balachandran, Prasanna V., 2026, "Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia", https://doi.org/10.18130/V3/9DMDUS, University of Virginia Dataverse, V1

Collection of 16 transition metal-substituted YSZ (yttria-stabilized zirconia) structure files and corresponding Yambo GW-BSE output logs, organized by dopant and nominal charge state. The crystal structure file can be used to replicate the GW-BSE calculation discussed in the manuscript, and the GW-BSE output log can be used a reference of GW-BSE c...

README.md

Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia

Markdown Text - 1.3 KB -

Co3YSZ.vasp

Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia

Unknown - 2.9 KB -

r-3D_BSE-diag_optics_dipoles_bss_bse

Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia

Unknown - 273.3 KB -

Cr3YSZ.vasp

Feb 13, 2026 - Data for: A Computational Dieke Design Map for Near-Infrared Optical Absorption in Transition Metal-Substituted Yttria-Stabilized Zirconia

Unknown - 2.9 KB -

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