| Machine learning prediction of the critical cooling rate for metallic glasses from expanded datasets and elemental features BT Afflerbach, C Francis, LE Schultz, J Spethson, V Meschke, E Strand, ... Chemistry of Materials 34 (7), 2945-2954, 2022 | 12 | 2022 |
| Exploration of characteristic temperature contributions to metallic glass forming ability LE Schultz, B Afflerbach, C Francis, PM Voyles, I Szlufarska, D Morgan Computational Materials Science 196, 110494, 2021 | 9 | 2021 |
| Microalloying effect in ternary Al-Sm-X (X= Ag, Au, Cu) metallic glasses studied by ab initio molecular dynamics J Xi, G Bokas, LE Schultz, M Gao, L Zhao, Y Shen, JH Perepezko, ... Computational Materials Science 185, 109958, 2020 | 9 | 2020 |
| Quantitative 10–50 kHz X-ray radiography of liquid spray distributions using a rotating-anode tube source BR Halls, JR Gord, LE Schultz, WC Slowman, MDA Lightfoot, S Roy, ... International Journal of Multiphase Flow 109, 123-130, 2018 | 9 | 2018 |
| Molecular simulation-derived features for machine learning predictions of metal glass forming ability BT Afflerbach, L Schultz, JH Perepezko, PM Voyles, I Szlufarska, ... Computational Materials Science 199, 110728, 2021 | 5 | 2021 |
| Development of a portable water quality sensor for river monitoring from small rafts J Schneider, LE Schultz, S Mancha, E Hicks, RN Smith OCEANS 2016 MTS/IEEE Monterey, 1-10, 2016 | 5 | 2016 |
| Molecular dynamic characteristic temperatures for predicting metallic glass forming ability LE Schultz, B Afflerbach, I Szlufarska, D Morgan Computational Materials Science 201, 110877, 2022 | 4 | 2022 |
| Design of torsional test stand for micro-Newton force detection LE Schultz, TJ Cogger, R Good, J Schneider, R Rothschild, W Nollet 2018 Aerodynamic Measurement Technology and Ground Testing Conference, 3737, 2018 | 2 | 2018 |
| Determining Domain of Machine Learning Models using Kernel Density Estimates: Applications in Materials Property Prediction LE Schultz, Y Wang, R Jacobs, D Morgan arXiv preprint arXiv:2406.05143, 2024 | 1 | 2024 |
| Foundry-ML-Software and Services to Simplify Access to Machine Learning Datasets in Materials Science K Schmidt, A Scourtas, L Ward, S Wangen, M Schwarting, I Darling, ... Journal of Open Source Software 9 (93), 5467, 2024 | 1 | 2024 |
| Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility R Jacobs, LE Schultz, A Scourtas, KJ Schmidt, O Price-Skelly, W Engler, ... arXiv preprint arXiv:2406.15650, 2024 | | 2024 |
| Ultra-fast Oxygen Conduction in Sill\'en Oxychlorides J Meng, MS Sheikh, LE Schultz, WO Nachlas, J Liu, MP Polak, R Jacobs, ... arXiv preprint arXiv:2406.07723, 2024 | | 2024 |
| Accelerating Ensemble Error Bar Prediction with Single Models Fits V Agrawal, S Zhang, LE Schultz, D Morgan arXiv preprint arXiv:2404.09896, 2024 | | 2024 |
| Discovery of New Fast Oxygen Conductors: Bi2MO4x (M= rare earth, X= halogen) Via Unsupervised Machine Learning J Meng, L Schultz, R Jacobs, D Morgan Electrochemical Society Meeting Abstracts 243, 2783-2783, 2023 | | 2023 |
| Optimization of a High-Speed X-Ray Imaging System for Studying Sprays LE Schultz, WC Slowman, TR Meyer, MN Slipchenko | | 2016 |
Dane MorganUniversity of Wisconsin, Materials Science在 wisc.edu 的电子邮件经过验证
