About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
A Generative AI Framework for Designing Nanoporous Silicon Nitride Membranes (NPM) with Optimized Mechanical Properties |
Author(s) |
Ali K. Shargh, Gregory R. Madejski, James L. McGrath, Niaz Abdolrahim |
On-Site Speaker (Planned) |
Ali K. Shargh |
Abstract Scope |
NPMs are ultrathin freestanding materials containing pores with tunable sizes. It is well-accepted that strength of NPM should improve significantly for applying them in life-saving applications like hemodialysis devices. However, traditional optimization methods cannot be used for empirical optimization of NPM strength as the number of tunable manufacturing parameters are too high. We develop an AI framework for design of NPMs with tunable performance. Our framework is trained on a large dataset containing thousands of NPMs with different microstructures that are labeled with strength from finite element simulations. We show that the framework could generate meaningful pore patterns corresponding to optimized strength values that are either absent or higher than initial dataset. Further evaluation of finite element simulations reveal that strength of the AI design is also higher than common commercialized pore pattern thanks to the decrease of stress concentration. We also validate these findings using experiments and atomistic simulations. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Other |