B12.4

Predicting Ionic Conductivity of Solid-State Battery Cathodes Using Machine Learning

Mai Le, University of Houston, United States; Hieu Le, Texas A&M, United States; Jiefu Chen, Xuqing Wu, Yan Yao, University of Houston, United States
Session:
B12: Numerical Methods Lecture
Track:
Commission B: Fields and Waves
Location:
Room 151
Presentation Time:
Fri, 12 Jan, 16:30 - 16:50 MST (UTC -7)
Session Chair:
Erdem Topsakal, VCU
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Presentation
Discussion
Resources
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Session B12
B12.1: Particle Trajectory Error in Finite Element Particle-in-Cell Kinetic Plasma Simulation
Haitham Saleh, Fernando Teixeira, The Ohio State University, United States
B12.2: Machine Learning Assisted Optimization Methods for Automated Antenna Design
Lauren Linkous, Erdem Topsakal, VCU, United States
B12.3: Adaptive Solution Space in Particle Swarm Optimization
Sahar Bagherkhani, University of California, Irvine, United States; Yahya Rahmat-Samii, University of California, Los Angeles, United States; Franco De Flaviis, University of California, Irvine, United States
B12.4: Predicting Ionic Conductivity of Solid-State Battery Cathodes Using Machine Learning
Mai Le, University of Houston, United States; Hieu Le, Texas A&M, United States; Jiefu Chen, Xuqing Wu, Yan Yao, University of Houston, United States
B12.5: Accuracy and Convergence Studies of Surrogate Methods for Uncertainty Quantification in FEM Scattering Computations
Branislav Notaros, Stephen Kasdorf, Colorado State University, United States
B12.6: CGFFT Iterative Solver of Integral Equations Launched on a Neural Network Platform
Botian Zhang, Yahya Rahmat-Samii, UCLA, United States
Contacts