Summary of Supply Risk-aware Alloy Discovery and Design, by Mrinalini Mulukutla (1) et al.
Supply Risk-Aware Alloy Discovery and Design
by Mrinalini Mulukutla, Robert Robinson, Danial Khatamsaz, Brent Vela, Nhu Vu, Raymundo Arróyave
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel risk-aware design approach integrates Supply-Chain Aware Design Strategies into the materials development process to address unsustainable and risk-prone solutions. It leverages language models and text analysis to predict materials feedstock supply risk indices. The approach employs Batch Bayesian Optimization (BBO) to navigate the multi-objective, multi-constraint design space, identifying Pareto-optimal high entropy alloys (HEAs) that balance performance objectives with minimized supply risk. A case study using the MoNbTiVW system demonstrates the efficacy of this approach in four scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to design materials that takes into account the risks involved in getting the materials needed to make them. They use special computer models and analysis tools to predict the chances of supply chain disruptions, then use another tool called Batch Bayesian Optimization to find the best combination of properties for the material. This means they can make sure the material is not only good at doing its job but also sustainable and affordable. The approach was tested on a specific system, MoNbTiTiVW, and showed promising results. |
Keywords
* Artificial intelligence * Optimization
