About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
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Symposium
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Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
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Presentation Title |
“Big Data” Characterization of Material Properties and High Temperature Kinetics |
Author(s) |
James Horwath, Peter W. Voorhees, Eric A. Stach |
On-Site Speaker (Planned) |
James Horwath |
Abstract Scope |
Competition between various coarsening and degradation mechanisms in supported nanoparticles inhibits the performance and stability of heterogeneous catalysts. While mean field models provide a physical understanding of the average behavior of the system, these models neglect local effects which are important at the nanoscale. By pairing in situ Transmission Electron Microscopy with unsupervised machine learning for automated image analysis, we quantify the degradation of supported Au nanoparticles at high temperature in real time. After developing a model to predict average particle growth as a function of chamber pressure and temperature, we use evolution trajectories from hundreds of individual nanoparticles to retrieve values for physical properties of the system with high accuracy. Further, by comparing extracted property values with those found in the literature, we characterize the system in terms of properties which are impossible to measure through traditional experiments, such as the distance over which nanoparticles interact. |
Proceedings Inclusion? |
Undecided |
Keywords |
Characterization, Machine Learning, Nanotechnology |