arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.12019cond-mat.mtrl-sci
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生成式深度学习作为高熵耐火合金逆向设计的工具

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

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

摘要:

生成式深度学习正在推动材料设计的新创新浪潮。在这篇文章中,我们讨论了这些方法的基本操作原理和合理设计的优势,通过一个案例研究的镜头耐火高熵合金的超高温应用。我们提出了我们的计算基础设施和工作流程的逆设计的新合金供电这些方法。我们的初步研究结果表明,生成模型可以学习复杂的关系,以产生新的需求,使它们成为材料信息学的一个有价值的工具。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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