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 (H_f) and decomposition (H_d) 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 H_f and 97.05% for H_d. 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 H_f and the H_d of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the H_f, it increases H_d, 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.

高温合金是制造飞机发动机高温部件不可或缺的材料。一类新型 γ/γ' Co-Al-W 合金的发现引发了人们对 Co 基高温合金的兴趣，人们希望超越其 Ni 基合金的固有限制。然而，用于设计和改进新型 γ/γ' 钴基高温合金的传统方法通常以其费力和资源密集型的性质为特征。在这项研究中，我们采用了密度泛函理论 （DFT） 和机器学习 （ML） 的耦合方法来预测和分析关键 γ' 相的稳定性，这有助于加快 γ/γ' 钴基合金的发现。通过高通量 DFT 计算获得了由数千个可靠的形成 （H_f） 和分解 （H_d） 能量组成的数据集。通过回归模型选择和特征工程，我们训练的随机森林 （RF） 模型实现了 H_f 的 98.07% 和 H _d 的预测精度 97.05%。利用训练有素的射频模型，我们预测了 Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb 系统中超过 150,000 个三元和四元 γ' 相的能量。能量分析表明，Ni、Nb、Ta、Ti 和 V 的存在显著降低了 γ' 的 H_f 和 H_d，而 Mo 和 W 通过增加这两个能量值来降低稳定性。有趣的是，虽然 Al 降低了 H_f，但它增加了 H_d，从而对 γ' 的稳定性产生了不利影响。 根据我们的知识应用特定域筛选，我们在 >150,000 种成分中确定了 1049 种可能形成稳定的 γ′ 相，主要分布在 11 个含铝系统和 25 个无铝系统中。结合 CALPHAD 方法的分析，我们实验合成了两种具有 γ/γ' 双相微观结构的新型 Co 基合金，证实了我们理论预测模型的可靠性。