Generative deep learning as a tool for inverse design of high-entropy refractory alloys
| Autor: | Shun Li Shang, Hui Sun, Shanshank Priya, Shuang Lin, Allison M. Beese, Zi Kui Liu, Wenjie Li, Adam M. Krajewski, Wesley F. Reinhart, Marcia Ahn, Jogender Singh, Arindam Debnath |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Condensed Matter - Materials Science
business.industry Computer science High entropy alloys Deep learning Novelty Materials informatics Inverse Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences Through-the-lens metering Workflow Systems engineering Artificial intelligence business Generative grammar |
| Popis: | Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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