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Fig. 1 Workflow of the present work.
来自中国厦门大学许伟伟和哈尔滨工业大学（深圳）刘兴军团队，提出了将DFT计算与机器学习模型相结合的搜索方法，可用DFT计算的能量数据来构建模型，从而提取有关能量和γ'相关系的完整信息，包括影响因素和竞争相，以及对超150000的合金体系进行快速的搜索，寻找出更多潜在的合金体系。他们发现了更多新型的γ/γ'钴基高温合金，并通过实验合成了两种新型合金，验证了机器学习模型在快速搜索新型合金的可行性以及模型预测的可靠性。
Fig. 2 Comparison of model accuracy before and after feature engineering.
该研究除了通过可靠的预测模型获取到更多新型钴基合金体系的信息外，还揭示了影响相合成和稳定性的因素，以及可能的竞争相，初步揭示了部分添加元素对γ'相的影响机制，与现有的部分研究结果一致：1) 训练的随机森林模型实现了形成能(Hf)预测精度98.07%和97.05%的分解能 (Hd)预测精度。2) Ni、Nb、Ta、Ti和V等元素增强了γ'相的稳定性，而Mo、W和Al通过增加分解能(Hd)对稳定性产生负面影响。3) 确定了1,049种有前途的候选物，主要分布在11个含铝和25个非铝合金体系中。实验表征了其中两种最佳体系，两种合金的 γ' 相稳定性超出了预期，即使在高温和长期时效处理下也能保持稳定。两种合金的最小密度约为7.90 g/cm³，优于大多数现有的钴基合金。该研究展示了机器学习在合金设计中的优越性，可以极大的加快新型γ/γ'钴基高温合金体系的发现。
Fig. 3 Experimental verification of the U01 and U02 includes CALPHAD evaluation, X-ray diffraction (XRD), and electron microscopy images (SEM).
该文近期发表于npj Computational Materials 10: 259 (2024)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning
ZhaoJing Han, ShengBao Xia, ZeYu Chen, Yihui Guo, ZhaoXuan Li, Qinglian Huang, Xing-Jun Liu & Wei-Wei Xu*
Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft engines. The discovery of a novel class of γ/γ′ Co-Al-W alloys has ignited a surge of interest in Co-based superalloys, with the aspiration to transcend the inherent constraints of their Ni-based counterparts. However, the conventional methodologies utilized in the design and advancement of new γ/γ′ Co-based superalloys are frequently characterized by their laborious and resource-intensive nature. In this study, we employed a coupled Density Functional Theory (DFT) and machine learning (ML) approach to predict and analyze the stability of the crucial γ′ phase, which is instrumental in expediting the discovery of γ/γ′ Co-based alloys. A dataset comprised of thousands of reliable formation (Hf) and decomposition (Hd) energies was obtained through high-throughput DFT calculations. Through regression model selection and feature engineering, our trained Random Forest (RF) model achieved prediction accuracies of 98.07% for Hf and 97.05% for Hd. Utilizing the well-trained RF model, we predicted the energies of over 150,000 ternary and quaternary γ′ phases within the Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb system. The energy analyses revealed that the presence of Ni, Nb, Ta, Ti, and V significantly reduced the Hf and the Hd of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the Hf, it increases Hd, thereby adversely affecting the stability of γ′. Applying domain-specific screening based on our knowledge, we identified 1049 out of >150,000 compositions likely to form stable γ′ phases, predominantly distributed across 11 Al-containing systems and 25 Al-free systems. Combining the analysis of CALPHAD method, we experimentally synthesized two new Co-based alloys with γ/γ′ dual-phase microstructures, corroborating the reliability of our theoretical prediction model.
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