Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys
Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys
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DOI：
10.1038/s41524-023-00993-x
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发表时间：
2023-04
影响因子：
9.7
通讯作者：
Chris Tandoc;Yong-Jie Hu;L. Qi;P. Liaw
中科院分区：
文献类型：
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作者：
Chris Tandoc;Yong-Jie Hu;L. Qi;P. Liaw
严重的晶格畸变是高熵合金（HEAs）的一个突出特征，被认为是导致这些合金许多特性的原因。然而，由于昂贵的实验或计算成本，晶格畸变的准确表征仍然很少，仅覆盖HEA巨大组成空间的一小部分。在这里，我们提出了一个物理信息统计模型，用于在10元素组成空间中有效地产生高通量晶格畸变预测难熔非稀/高熵合金（RHEAs）。该模型通过基于原子间键的物理性质而不是基于纯元素的原子尺寸失配进行预测，从而提高了快速估计晶格畸变的精度。通过对晶格畸变的建模，实现了RHEAs屈服强度的预测模型，并得到了多组实验数据的验证。结合我们之前的固有延性模型，展示了一个数据挖掘设计框架，用于有效地探索强延性单相RHEAs。
Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a reason for many of those alloys’ properties. Nevertheless, accurate characterizations of lattice distortion are still scarce to only cover a tiny fraction of HEA’s giant composition space due to the expensive experimental or computational costs. Here we present a physics-informed statistical model to efficiently produce high-throughput lattice distortion predictions for refractory non-dilute/high-entropy alloys (RHEAs) in a 10-element composition space. The model offers improved accuracy over conventional methods for fast estimates of lattice distortion by making predictions based on physical properties of interatomic bonding rather than atomic size mismatch of pure elements. The modeling of lattice distortion also implements a predictive model for yield strengths of RHEAs validated by various sets of experimental data. Combining our previous model on intrinsic ductility, a data mining design framework is demonstrated for efficient exploration of strong and ductile single-phase RHEAs.