“高熵合金设计的预测和启发式框架：整合固溶强化与机器学习”[J]。合金。Compd. 1027 (2025) 180484]

IF 5.8 2区材料科学 Q2 CHEMISTRY, PHYSICAL

Journal of Alloys and Compounds Pub Date : 2025-05-26 DOI:10.1016/j.jallcom.2025.181117

Zheng Zhang, Yuanpei Meng, Zongyu Zhang, Yansong Yang, Ying Chen, Chuanting Wang, Yong He

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引用次数: 0

摘要

图6(b)。在出版的版本中没有出现。更正：图6应如下所示。下载：下载高分辨率图片（220KB）下载：下载全尺寸图片6. 生成方程的ST复杂度分布和目标公式的拟合。(a) ST算法开发得到的公式训练拟合与公式复杂度的关系。红色的叉是选择的目标公式。(b)通过式(8)对数据集的计算值。

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Corrigendum to “Predictive and heuristic framework for high entropy alloys design: Integrating solid solution strengthening with machine learning” [J. Alloy. Compd. 1027 (2025) 180484]

The authors regret “Fig. 6(b). was not shown in the published version. Correction: Fig. 6 should read as follows.”.

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来源期刊

Journal of Alloys and Compounds 工程技术-材料科学：综合

CiteScore

11.10

自引率

14.50%

发文量

5146

审稿时长

67 days

期刊介绍： The Journal of Alloys and Compounds is intended to serve as an international medium for the publication of work on solid materials comprising compounds as well as alloys. Its great strength lies in the diversity of discipline which it encompasses, drawing together results from materials science, solid-state chemistry and physics.