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arXiv 2408.06237cond-mat.mtrl-sci
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深度学习加速难熔多主元合金的相预测

Deep Learning Accelerated Phase Prediction of Refractory Multi-Principal Element Alloys

A. K. Shargh, C. D. Stiles, J. A. El-Awady

AI总结:

本文提出基于CALPHAD数据库训练的深度学习框架,可在Ti、Fe、Al、V、Ni、Nb、Zr元素空间内以约90%准确率预测难熔多主元合金的相,并分析了域外性能下降原因及改进策略。

摘要:

难熔多主元合金(RMPEAs)力学性能的可调性使其在众多高温应用中具有吸引力。已有充分研究表明,RMPEAs的相稳定性控制着其力学性能。在本研究中,我们开发了一个深度学习框架,该框架在源自CALPHAD的数据库上进行训练,能够在Ti、Fe、Al、V、Ni、Nb和Zr元素空间内以约90%的准确率预测RMPEAs的相,最多可预测八个相。我们进一步研究了深度学习模型在预测具有新元素集的RMPEA相时域外性能较低的原因,并提出了一种缓解该性能不足的策略。

英文摘要:

The tunability of the mechanical properties of refractory multi-principal-element alloys (RMPEAs) make them attractive for numerous high-temperature applications. It is well-established that the phase stability of RMPEAs control their mechanical properties. In this study, we develop a deep learning framework that is trained on a CALPHAD-derived database that is predictive of RMPEAs phases with high accuracy up to eight phases within the elemental space of Ti, Fe, Al, V, Ni, Nb, and Zr with an accuracy of approximately 90%. We further investigate the causes for the low out of domain performance of the deep learning models in predicting phases of RMPEA with new elemental sets and propose a strategy to mitigate this performance shortfall.

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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