| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Accelerated Discovery and Insertion of Next Generation Structural Materials
|
| Presentation Title |
Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning |
| Author(s) |
Terrance Life, Shankarachary Ragi, Bharat Jasthi, Ananth Kandadai |
| On-Site Speaker (Planned) |
Terrance Life |
| Abstract Scope |
The traditional method to develop and improve materials with desired properties is through trial and error, a time-consuming process requiring domain experts to propose, fabricate, and test numerous potential materials. Our proposed method to accelerate this is to utilize combinatorial deposition and Bayesian active learning to efficiently solve a multi-objective optimization problem for thin-film materials. We demonstrate our method through the fabrication of a copper-silver-nickel alloy in which the percentages of each element vary throughout the sample. This allows the sample to represent a range of potential alloys which our method explores to optimize the conductivity and surface hardness, guided by a Bayesian framework based on Gaussian Process models. The predictions from these models permit the system to selectively test only the alloys most likely to yield the optimal properties. This concludes with an analysis of the global Pareto front, which guides the synthesis of future test samples. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Characterization |