NpjComput.Mater.：機器學習鐵電材料設計：相穩定性評估與新摻雜劑識別

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

HfO₂鐵電薄膜材料在資訊儲存、感測技術、類腦計算以及仿生電子器件等領域均有重要應用，然而目前學術界對HfO₂薄膜的鐵電相變和相穩定機理尚無統一的認識，薄膜中鐵電相的調控和薄膜鐵電效能穩定性的控制仍是氧化鉿基鐵電薄膜研究領域的重大挑戰。

Fig. 1 The working framework of this study.

近幾年，機器學習在加速材料最佳化設計領域展示出強大的能力，除了能準確進行多因素綜合下的效能預測外，還能提供材料設計最佳化新的物理視角。來自湘潭大學的燕少安教授、唐明華教授、朱穎方博士與復旦大學盧紅亮教授合作，將高通量第一性原理計算、機器學習及實驗驗證相結合，採用SISSO (確定獨立性篩選和稀疏化運算元)策略建立了多階段材料設計框架，從而構建了一個高可靠性和高準確性的鐵電HfO₂材料機器學習模型，揭示了HfO₂材料的鐵電相穩相機理，提出了其相穩定性評價方法。

Fig. 2 Ferroelectric phase fraction in the doped HfO₂system obtained through Boltzmann distribution theory.

在機器學習模型的預測下，他們使用鎵(Ga)作為一種全新的摻雜劑，在實驗中成功製備了HfGaO鐵電薄膜，獲得了不同鎵摻雜濃度下鐵電效能和鐵電相的變化規律，這充分證明了所構建的機器學習模型能夠在龐大的化學空間中鑑別有價值的鐵電氧化鉿摻雜元素。

圖2 機器學習模型的效能表現，包括材料是否具有鐵電相結構的預測，摻雜濃度變化的影響，物理特徵之間的皮爾遜相關係數，模型在預測相能量差和極化強度時的迴歸效能。

圖3 透過實驗驗證了Ga摻雜HfO₂薄膜的鐵電效能，實驗測定的鐵電相分數和極化效能隨鎵摻雜濃度的變化趨勢與機器學習的預測結果高度一致。

（原文連結：https://www.nature.com/articles/s41524-024-01510-4）

該研究提出的多階段材料設計框架為HfO₂基鐵電材料的設計和效能預測提供了新的思路。該文近期發表於npj Computational Materials11,: 2 (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。

Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials

Shaoan Yan, Yingfang Zhu, Minghua Tang, and Hongliang Lu

In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO2 based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO2. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO2 materials, offering fresh insights into the design and performance prediction of HfO2 ferroelectric thin films.