Constrained by the inefficiency of traditional trial-and-error methods, especially when dealing with thousands of candidate materials, the swift discovery of materials with specific properties remains a central challenge in contemporary materials research. This study employed an artificial intelligence-driven materials design framework for identifying dopants that impart antiferroelectric properties to HfO₂ materials. This strategy integrates density functional theory (DFT) with machine learning (ML) techniques to swiftly screen HfO₂ materials exhibiting stable antiferroelectric properties based on the critical electric field. This approach aims to overcome the high cost and lengthy cycles associated with traditional trial-and-error and experimental methods. Among 30 undeveloped dopants, four candidate dopants demonstrating stable antiferroelectric properties were identified. Subsequent DFT analysis highlighted the Ga dopant, which displayed favorable characteristics such as a small volume change, minimal lattice deformation, and a low critical electric field after incorporation into hafnium oxide. These findings suggest the potential for stable antiferroelectric performance. Essentially, we established a correlation between the physical characteristics of hafnium oxide dopants and their antiferroelectric performance. The approach facilitates large-scale ML predictions, rendering it applicable to a broad spectrum of functional material designs.

中文翻译：

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人工智能揭示的氧化铪基反铁电材料的相变机理及性能预测


受制于传统试错法的低效率，尤其是在处理数千种候选材料时，快速发现具有特定特性的材料仍然是当代材料研究的核心挑战。本研究采用人工智能驱动的材料设计框架来识别赋予 HfO₂ 材料反铁电性能的掺杂剂。该策略将密度泛函理论 （DFT） 与机器学习 （ML） 技术相结合，以基于临界电场快速筛选表现出稳定反铁电特性的 HfO₂ 材料。这种方法旨在克服与传统试错法和实验方法相关的高成本和漫长周期。在 30 种未显影的掺杂剂中，确定了 4 种表现出稳定反铁电性能的候选掺杂剂。随后的 DFT 分析突出了 Ga 掺杂剂，它在掺入氧化铪后表现出良好的特性，例如体积变化小、晶格变形最小和临界电场低。这些发现表明了稳定的反铁电性能的潜力。从本质上讲，我们建立了氧化铪掺杂剂的物理特性与其抗铁电性能之间的相关性。该方法有助于大规模 ML 预测，使其适用于广泛的功能性材料设计。