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HfO2铁电薄膜材料在信息存储、传感技术、类脑计算以及仿生电子器件等领域均有重要应用，然而目前学术界对HfO2薄膜的铁电相变和相稳定机理尚无统一的认识，薄膜中铁电相的调控和薄膜铁电性能稳定性的控制仍是氧化铪基铁电薄膜研究领域的重大挑战。
Fig. 1 The working framework of this study.
近几年，机器学习在加速材料优化设计领域展示出强大的能力，除了能准确进行多因素综合下的性能预测外，还能提供材料设计优化新的物理视角。来自湘潭大学的燕少安教授、唐明华教授、朱颖方博士与复旦大学卢红亮教授合作，将高通量第一性原理计算、机器学习及实验验证相结合，采用SISSO (确定独立性筛选和稀疏化算子)策略建立了多阶段材料设计框架，从而构建了一个高可靠性和高准确性的铁电HfO2材料机器学习模型，揭示了HfO2材料的铁电相稳相机理，提出了其相稳定性评价方法。
Fig. 2 Ferroelectric phase fraction in the doped HfO2system obtained through Boltzmann distribution theory.
在机器学习模型的预测下，他们使用镓(Ga)作为一种全新的掺杂剂，在实验中成功制备了HfGaO铁电薄膜，获得了不同镓掺杂浓度下铁电性能和铁电相的变化规律，这充分证明了所构建的机器学习模型能够在庞大的化学空间中鉴别有价值的铁电氧化铪掺杂元素。
图2 机器学习模型的性能表现，包括材料是否具有铁电相结构的预测，掺杂浓度变化的影响，物理特征之间的皮尔逊相关系数，模型在预测相能量差和极化强度时的回归性能。
图3 通过实验验证了Ga掺杂HfO2薄膜的铁电性能，实验测定的铁电相分数和极化性能随镓掺杂浓度的变化趋势与机器学习的预测结果高度一致。
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.