Liu Yongtao, Morozovska Anna N, Eliseev Eugene A, Kelley Kyle P, Vasudevan Rama, Ziatdinov Maxim, Kalinin Sergei V
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA.
Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine.
Patterns (N Y). 2023 Mar 10;4(3):100704. doi: 10.1016/j.patter.2023.100704.
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore the bias-induced transformations that underpin the functionality of broad classes of devices and materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of a broad range of control parameters, leading to experimentally intractable scenarios. Meanwhile, often these behaviors are understood within potentially competing theoretical hypotheses. Here, we develop a hypothesis list covering possible limiting scenarios for domain growth in ferroelectric materials, including thermodynamic, domain-wall pinning, and screening limited. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching, and the results indicate that domain growth is ruled by kinetic control. We note that the hypothesis learning can be broadly used in other automated experiment settings.
利用假设学习驱动的自动扫描探针显微镜（SPM），我们探索了偏置诱导的转变，这些转变是从电池、忆阻器到铁电体和反铁电体等广泛类别的器件和材料功能的基础。这些材料的优化和设计需要在纳米尺度上探究这些转变的机制，作为广泛控制参数的函数，这导致了实验上难以处理的情况。同时，这些行为通常在潜在相互竞争的理论假设中得到理解。在这里，我们制定了一个假设列表，涵盖了铁电材料中畴生长的可能限制情况，包括热力学、畴壁钉扎和屏蔽限制。假设驱动的SPM自主识别偏置诱导的畴切换机制，结果表明畴生长受动力学控制。我们注意到，假设学习可以广泛应用于其他自动实验设置中。
