高熵合金自2004年問世以來，因其卓越的機械和物理性能引起廣泛的研究興趣。與傳統合金相比，高熵合金通常包含五種以上元素,勢能面比較複雜，潛在的亞穩結構較多，形成哪一種亞穩結構通常取決於具體的實驗條件。由於這些複雜性，預測高熵合金的相結構至今仍是一個難題。
來自中國香港城市大學工程學院力學工程系的楊勇團隊，基於人工神經網絡等三種不同的算法開發了機器學習模型，用於指導高熵合金的相結構設計。他們首先採用了一組包含601個多元合金數據的數據集訓練了模型，之後基於該模型定量評估了文獻中已有的高熵合金相結構的設計規則，並探索提出了一組全新的設計參數。這些新參數與多組元系統的勢能面波動相關聯，因此大大提高了機器學習模型的準確性。為了驗證模型的可靠性，他們基於Fe-Cr-Ni-Zr-Cu多元體系開展了一系列實驗，包括鑄造、熔融紡絲和共濺射等，並設計出了一系列新型合金，實驗結果與理論預測高度吻合。該研究表明，基於機器學習技術有望發展高熵或多組元合金設計的新工具。
該文近期發表於npj Computational Materials 5: 128 (2019)，英文標題與摘要如下，點擊https://www.nature.com/articles/s41524-019-0265-1可以自由獲取論文PDF。
Machine learning guided appraisal and exploration of phase design for high entropy alloys
Ziqing Zhou, Yeju Zhou, Quanfeng He, Zhaoyi Ding, Fucheng Li & Yong Yang
High entropy alloys (HEAs) and compositionally complex alloys (CCAs) have recently attracted great research interest because of their remarkable mechanical and physical properties. Although many useful HEAs or CCAs were reported, the rules of phase design, if there are any, which could guide alloy screening are still an open issue. In this work, we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning (ML) algorithms. Based on the artificial neural network algorithm, we were able to derive and extract a sensitivity matrix from the ML modeling, which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase, such as solid solution, intermetallic, or amorphous phase.Furthermore, we explored the use of an extended set of new design parameters, which had not been considered before, for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule, we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system. The outcomes of our experiments agree reasonably well with our predictions, which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.
閱讀更多 知社學術圈 的文章
