NpjComput.Mater.：如何加速發現新型鈷基高溫合金？正是你想到的AI！

海歸學者發起的公益學術平臺

分享資訊，整合資源

交流學術，偶爾風月

高溫合金是航空發動機熱端部件的關鍵材料，其中鎳基高溫合金因其獨特的γ/γ′雙相微觀結構和優異的高溫機械效能而被廣泛應用。然而，鎳基高溫合金的熱耐受性極限促使人們對下一代高效能合金材料的探索產生了濃厚興趣。新型γ/γ′ Co-Al-W合金的發現重新點燃了對鈷基高溫合金的關注，展示出相對於傳統鎳基合金的潛在優勢。在尋找新的γ/γ′鈷基高溫合金體系的過程中，傳統方法常因試錯實驗帶來的開發週期長、成本高等挑戰而受限。

Fig. 1 Workflow of the
present work.

來自中國廈門大學許偉偉和哈爾濱工業大學（深圳）劉興軍團隊，提出了將DFT計算與機器學習模型相結合的搜尋方法，可用DFT計算的能量資料來構建模型，從而提取有關能量和γ'相關係的完整資訊，包括影響因素和競爭相，以及對超150000的合金體系進行快速的搜尋，尋找出更多潛在的合金體系。他們發現了更多新型的γ/γ'鈷基高溫合金，並透過實驗合成了兩種新型合金，驗證了機器學習模型在快速搜尋新型合金的可行性以及模型預測的可靠性。

Fig. 2Comparison of model accuracy before and after feature engineering.

該研究除了透過可靠的預測模型獲取到更多新型鈷基合金體系的資訊外，還揭示了影響相合成和穩定性的因素，以及可能的競爭相，初步揭示了部分新增元素對γ'相的影響機制，與現有的部分研究結果一致：1) 訓練的隨機森林模型實現了形成能(H_f)預測精度98.07%和97.05%的分解能 (H_d)預測精度。2) Ni、Nb、Ta、Ti和V等元素增強了γ'相的穩定性，而Mo、W和Al透過增加分解能(H_d)對穩定性產生負面影響。3) 確定了1,049種有前途的候選物，主要分佈在11個含鋁和25個非鋁合金體系中。實驗表徵了其中兩種最佳體系，兩種合金的 γ' 相穩定性超出了預期，即使在高溫和長期時效處理下也能保持穩定。兩種合金的最小密度約為7.90 g/cm³，優於大多數現有的鈷基合金。該研究展示了機器學習在合金設計中的優越性，可以極大的加快新型γ/γ'鈷基高溫合金體系的發現。

Fig. 3 Experimental
verification of the U01 and U02 includes CALPHAD evaluation, X-ray diffraction
(XRD), and electron microscopy images (SEM).

該文近期發表於npj Computational Materials10: 259 (2024)，英文標題與摘要如下，點選左下角“閱讀原文”可以自由獲取論文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.