Artificial Intelligence and Machine Learning for Materials

Monday, September 12, 2022: 3:00 PM-5:00 PM
Convention Center: 273 (Ernest N. Morial Convention Center)
Prof. Michael S. Titus, Ph.D., Purdue University
3:00 PM
Enabling high-strength and oxidation-resistant refractory complex, concentrated alloys via a machine learning for accelerated materials discovery framework
Prof. Michael S. Titus, Ph.D., Purdue University
3:20 PM
Machine Learning for Joint Quality Performance-Determining relationship between process parameters and weld microstructure of Al/steel resistance spot welds
Ms. Deborah K Fagan, Pacific Northwest National Laboratory; Dr. Moses Obiri, Pacific Northwest National Laboratory; Dr. WoongJo Choi, Pacific Northwest National Laboratory; Dr. Keerti Kappagantula, Pacific Northwest National Laboratory; Dr. Blair Carlson, General Motors Global R&D; Dr. Hassan Ghassemi-Armaki, General Motors Global R&D
3:40 PM
Machine Learning for Joint Quality Performance-Determining relationship between process parameters and joint performance of Al/steel resistance spot welds
Dr. Moses Obiri, Pacific Northwest National Laboratory; Ms. Deborah K Fagan, Pacific Northwest National Laboratory; Dr. Keerti Kappagantula, Pacific Northwest National Laboratory; Dr. Blair Carlson, General Motors Global R&D; Dr. Hassan Ghassemi-Armaki, General Motors Global R&D; Mr. Alejandro Ojeda, Pacific Northwest National Laboratory; Dr. Lisa Bramer, Pacific Northwest National Laboratory
4:00 PM
Atomistic Line Graph Neural Network for improved materials property predictions
Dr. Kamal Choudhary, National Institute of Standards and Technology
4:20 PM
Literature-ML-Validation Automation in Ecosystem of a HEA Database
Mr. Adam M. Krajewski, The Pennsylvania State University; Prof. Zi-Kui Liu, The Pennsylvania State University
4:40 PM
Machine Learning assisted Prediction of Heat Transfer Coefficients during Quenching
Dr. Imre Felde, University of Obuda
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