NpjComput.Mater.：雙相難熔高熵合金設計：AI驅動

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

難熔高熵合金（RHEAs）因其在高溫環境下展現出的卓越強度而備受矚目，但普遍存在延展性不足的問題，這一矛盾極大制約了其工程化應用。傳統RHEAs主要由九種難熔金屬元素組成，通常呈現體心立方（BCC）結構，在高溫下表現出優異的強度，但在提升強度的同時往往犧牲了延展性，形成了典型的強度-延展性權衡。為打破這一瓶頸，研究人員嘗試引入面心立方（FCC）結構作為第二相，開發兼具高強度和良好延展性的新型雙相RHEAs。然而，RHEAs的成分極其複雜，傳統的“試錯法”實驗不僅耗時費力，也難以在龐大的成分空間中高效篩選出理想合金體系，這使得強韌協同的精準設計成為亟待破解的核心科學問題。

來自北京科技大學周香林教授團隊聯合中科院寧波材料所張咪娜副研究員團隊，基於CALPHAD相圖計算、機器學習建模與實驗方法提出了一套AI驅動合金設計策略，用以探索BCC/FCC雙相難熔高熵合金的成分設計空間。

Fig.1. AI-driven design strategy for BCC/FCC dual-phase refractory high-entropy alloys.

研究團隊首先利用CALPHAD相圖計算系統分析了合金元素的二元相形成規律，從中篩選出13種潛在的“液相分離”型BCC/FCC雙相RHEA，以充實機器學習模型的訓練資料集。

Fig.2. Thermodynamic simulations of equilibrium and nonequilibrium solidification in BCC/FCC dual-phase RHEAs.

在此基礎上，團隊進行了兩輪二分類任務：一是判斷“單相/多相”結構，二是區分“固溶體/金屬間化合物”。最終，成功訓練出兩個神經網路模型，準確率分別達到89.52%和89.83%。依託這兩個模型研究團隊從504種新型RHEA體系中預測出51種具有BCC/FCC雙相結構的新合金，並對部分合金進行了實驗驗證。

Fig.3. Neural network model developed for predicting phase structures in high-entropy alloys.

透過電弧熔鍊製備的合金(NiMoVW合金)表現出精細的樹枝狀組織，這不僅證實了模型的預測能力，也展示了BCC/FCC雙相結構在微觀尺度上的獨特組織特徵。

Fig.4. Comparison of predicted phase structures with experimental validation.

這是目前首次成功實現BCC/FCC雙相RHEA的組分設計，為打破RHEA的強度與延展性瓶頸提供了新的材料設計思路。該文近期發表於npj Computational Materials11, 105 (2025)，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。

Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods

Longjun He1, Chaoyue Wang1, Mina Zhang2*,Jinghao Li3, Tianlun Chen1 & Xianglin Zhou1*

Refractory high-entropy alloys (RHEAs) typically exhibit a body-centered cubic (BCC) structure with excellent strength but poor ductility, which limits their practical applications. In this study, we designed BCC/FCC dual-phase RHEAs through phase diagram calculations and neural network modeling. The analysis of the binary phase formation relationships among alloying elements enabled the preliminary screening and inclusion of 13 liquid-phase-separated BCC/FCC dual-phase RHEAs in the training dataset for the machine learning model. Two strategic binary classifications of this dataset were conducted on HEAs to identify their “multiphase” and “solid solution” structures. Consequently, two neural network models were trained, achieving accuracies of 89.52% and 89.83%, respectively. These models predicted 51 BCC/FCC dual-phase RHEAs among 504 novel RHEAs, representing the first successful compositional design of metastable BCC/FCC dual-phase RHEAs. The arc-melted alloys exhibited refined dendritic structure. This study provides valuable insights for the tailored design of novel multi-phase RHEAs to achieve specific targeted properties.