Back to Top ↑2023 Symposium Sessions |
| Tuesday June 20 |
| 10:30 | Molecular Modeling |
| 1:30 | AI for Materials |
| 4:00 | AI for Materials - Posters |
|
2023 Symposium Program |
| Tuesday June 20 |
|
| 10:30 | Molecular Modeling | Chesapeake A |
| Session chair: Jan-Willem Handgraaf, Siemens Digital Industries Software, NE |
| 10:30 | Molecular Dynamics Simulation of the Dynamic Hydration Layer in a Polyzwitterionic Polymer
J.A. Clark, V.M. Prabhu, J.F. Douglas, National Institute of Standards and Technology, US |
| 10:55 | How molecular simulations can help to energetic transition?
D. Pantano, Total Energies, US |
| 11:20 | Molecular Dynamic Simulation Study on the Effects of Moisture Content on the Water Activity and Glass Transition Temperature of Food Carbohydrates
L. Abudour, General Mills, US |
| 11:45 | First-principles study of the tritium diffusion and formation in γ-LiAlO2 pellets
Y. Duan, T. Jia, H. Paudel, Y.-L. Lee, D. Senor, A.M. Casella, National Energy Technology Laboratory, US |
|
| 1:30 | AI for Materials | Chesapeake A |
| Session chair: Jan-Willem Handgraaf, Siemens Digital Industries Software, NE |
| 1:30 | Generative Models for Synthetically Accessible Polymers
N.E. Jackson, University of Illinois Urbana-Champaign, US |
| 1:55 | Accelerating Materials Discovery and Design using AI and Machine Learning
P.S. Dutta, A. Koneru, D. Sanpui, A. Chandra, H. Chan, S. Manna, S. Banik, T.D. Loeffler, S.K.R.S. Sankaranarayanan, University of Illinois at Chicago, US |
| 2:15 | Robocoater: Automated, Multi-Modal Optical Characterization Platform for Performing Closed-loop Bayesian Optimization of Thin-Film Hybrid Perovskite for PV Application
N. Woodward, B. Guo, M. Chauhan, M. Abolhasani, K. Rayes, A. Amassian, North Carolina State University, US |
| 2:35 | Machine Learning-Driven Automated Scanning Probe Microscopy
Y. Liu, K.P. Kelley, R.K. Vasudevan, M. Ziatdinov, S.V. Kalinin, Oak Ridge National Laboratory, US |
| 2:55 | Machine learning accelerated computational design of materials and processes
T.P.M. Goumans, M. Hellström, P.S.N. Onofrio, N. Aguirre, R. Rüger, Software for Chemistry & Materials, NL |
|
| 4:00 | AI for Materials - Posters | Expo Hall AB |
| Metal Hydride Composition-Derived Parameters as Machine Learning Features for Alloy Design and H2 Storage
S. Nations, T. Nandi, A. Ramazani, S. Wang, Y. Duan, National Energy Technology Laboratory, US |
| Single-Electron Reservoir Computing Circuit with Online Learning
S. Watanabe, T. Oya, Yokohama National University, JP |
| AI-driven Part Printability Recommendation System for Additive Manufacturing
J.A. Steets, D. Mooney, B. O’Briant, Illumination Works, LLC., US |
| Relatable Explanations For AI Applications
J. Tan, National University of Singapore, SG |
| Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials
A. Ziaullah, S. Chawla, F. El Mellouhi, Hamad Bin Khalifa University, QA |
| Accelerating Materials discovery: Best practices for Research Data Management Strategies
J. Medina, A. Wahab Ziaullah, E-T. Bentria, H. Park, I.E. Castelli, A. Shaon, H. Bensmail, F. El-Mellouhi, Hamad Bin Khalifa University, QA |
| Physics-informed machine learning prediction of Curie temperature of rare-earth magnetic materials
P. Singh, T. Del Rose, A. Palasyuk, Y. Mudryk, Ames National Laboratory, US |
| AI-Based Linearization Schemes for 5G/6G Fiber/Wireless Systems
L.A. Melo Pereira, L..L. Mendes, C.J. Albanez Bastos Filho, A. Cerqueira Sodré Junior, National Institute of Telecommunications (Inatel), BR |
| Design and Implementation of a Modified Shortest Path Algorithm for Package Delivery
B. Abegaz, Loyola University Chicago, US |
| Developing a Multi-Sensing Platform for a Six Degrees of Freedom Industrial Robot
B. Abegaz, Loyola University Chicago, US |
|
Generative Models for Synthetically Accessible Polymers
