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Dane Morgan
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2020 – today
- 2024
- [j3]K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael C. Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik:
Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. J. Open Source Softw. 9(93): 5467 (2024) - [d5]K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik:
Foundry-ML (latest). Zenodo, 2024 - [d4]K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik:
Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Version v0.5.0. Zenodo, 2024 [all versions] - [d3]K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik:
Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Version 2. Zenodo, 2024 [all versions] - [d2]K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik:
Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Version v0.7.4. Zenodo, 2024 [all versions] - [i13]Vidit Agrawal, Shixin Zhang, Lane E. Schultz, Dane Morgan:
Accelerating Ensemble Error Bar Prediction with Single Models Fits. CoRR abs/2404.09896 (2024) - [i12]Lane E. Schultz, Yiqi Wang, Ryan Jacobs, Dane Morgan:
Determining Domain of Machine Learning Models using Kernel Density Estimates: Applications in Materials Property Prediction. CoRR abs/2406.05143 (2024) - [i11]Matthew J. Lynch, Ryan Jacobs, Gabriella Bruno, Priyam Patki, Dane Morgan, Kevin G. Field:
Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring. CoRR abs/2408.01558 (2024) - [i10]Ryan Jacobs, Maciej P. Polak, Lane E. Schultz, Hamed Mahdavi, Vasant G. Honavar, Dane Morgan:
Regression with Large Language Models for Materials and Molecular Property Prediction. CoRR abs/2409.06080 (2024) - [i9]Quanliang Liu, Maciej P. Polak, So Yeon Kim, MD Al Amin Shuvo, Hrishikesh Shridhar Deodhar, Jeongsoo Han, Dane Morgan, Hyunseok Oh:
Beyond designer's knowledge: Generating materials design hypotheses via large language models. CoRR abs/2409.06756 (2024) - 2023
- [j2]Ryan Jacobs, Philip E. Goins, Dane Morgan:
Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap. Mach. Learn. Sci. Technol. 4(4): 45060 (2023) - [i8]Maciej P. Polak, Shrey Modi, Anna Latosinska, Jinming Zhang, Ching-Wen Wang, Shanonan Wang, Ayan Deep Hazra, Dane Morgan:
Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models. CoRR abs/2302.04914 (2023) - [i7]Maciej P. Polak, Dane Morgan:
Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering - Example of ChatGPT. CoRR abs/2303.05352 (2023) - 2022
- [d1]Dongzheng Chen, Ryan Jacobs, Dane Morgan, John Booske:
Data for: Impact of Non-Uniform Thermionic Emission on the Transition Behavior between Temperature- and Space-Charge-Limited Emission. IEEE DataPort, 2022 - [i6]Mingren Shen, Dina Sheyfer, Troy David Loeffler, Subramanian K. R. S. Sankaranarayanan, G. Brian Stephenson, Maria K. Y. Chan, Dane Morgan:
Machine learning for interpreting coherent X-ray speckle patterns. CoRR abs/2211.08194 (2022) - 2021
- [i5]Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G. Field, Dane Morgan:
A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations. CoRR abs/2108.08882 (2021) - [i4]Mingren Shen, Guanzhao Li, Dongxia Wu, Yuhan Liu, Jacob Greaves, Wei Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan Sanchez, Oigimer Torres, Wei Li, Kevin Field, Dane Morgan:
Multi defect detection and analysis of electron microscopy images with deep learning. CoRR abs/2108.08883 (2021) - [i3]Ryan Jacobs, Mingren Shen, Yuhan Liu, Wei Hao, Xiaoshan Li, Ruoyu He, Jacob R. C. Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G. Field, Dane Morgan:
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs. CoRR abs/2110.08244 (2021) - 2020
- [i2]Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan:
Assessing Graph-based Deep Learning Models for Predicting Flash Point. CoRR abs/2002.11315 (2020) - [i1]Nick Lawrence, Mingren Shen, Ruiqi Yin, Cloris Feng, Dane Morgan:
Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation. CoRR abs/2010.15315 (2020)
2000 – 2009
- 2006
- [j1]Gevorg Grigoryan, Fei Zhou, Steve R. Lustig, Gerbrand Ceder, Dane Morgan, Amy E. Keating:
Ultra-Fast Evaluation of Protein Energies Directly from Sequence. PLoS Comput. Biol. 2(6) (2006)
Coauthor Index
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