Generative deep learning as a tool for inverse design of high-entropy refractory alloys

2021 · Generative modeling, High entropy alloys · A Debnath

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Arindam Debnath, Adam M Krajewski, Hui Sun, Shuang Lin, Marcia Ahn, Wenjie Li, Shanshank Priya, Jogender Singh, Shunli Shang, Allison M Beese, Zi-Kui Liu, Wesley F Reinhart

Journal of Materials Informatics (2021)

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Abstract

Generative deep learning is powering a wave of new innovations in materials design. This article discusses 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 to generate novelty on demand, making them a valuable tool for materials informatics.