Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films
Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films
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DOI：
10.1002/adma.202002425
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发表时间：
2020-08-14
影响因子：
29.4
通讯作者：
Bassiri-Gharb, Nazanin
中科院分区：
文献类型：
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作者：
Griffin, Lee A.;Gaponenko, Iaroslav;Bassiri-Gharb, Nazanin
机器学习技术越来越多地应用于材料研究中复杂行为的分析。这些技术通常用于识别大型多维数据集中的基本行为，并且严格基于数学模型。因此，如果没有固有的物理或化学意义或限制，它们很容易出现有偏见的解释。材料科学中机器学习结果的可解释性，特别是材料的功能，可以通过物理洞察和仔细的数据处理得到极大的提高。使用维度堆叠等技术可以提供急需的物理和化学约束，而正确理解模型参数所施加的假设可以帮助避免过度解释。这些概念通过最近报道的 PbZr(0.2)Ti(0.8)O(3) 薄膜铁电开关实验的应用得到了说明。通过系统分析和物理约束的引入，有人认为，所出现的行为不一定是由于先前提出的奇异机制，而是通过叠加非铁电现象（例如电化学变形、静电相互作用和/或电荷注入）的经典铁电开关来很好地描述。
Machine-learning techniques are more and more often applied to the analysis of complex behaviors in materials research. Frequently used to identify fundamental behaviors within large and multidimensional datasets, these techniques are strictly based on mathematical models. Thus, without inherent physical or chemical meaning or constraints, they are prone to biased interpretation. The interpretability of machine-learning results in materials science, specifically materials' functionalities, can be vastly improved through physical insights and careful data handling. The use of techniques such as dimensional stacking can provide the much needed physical and chemical constraints, while proper understanding of the assumptions imposed by model parameters can help avoid overinterpretation. These concepts are illustrated by application to recently reported ferroelectric switching experiments in PbZr(0.2)Ti(0.8)O(3)thin films. Through systematic analysis and introduction of physical constraints, it is argued that the behaviors present are not necessarily due to exotic mechanisms previously suggested, but rather well described by classical ferroelectric switching superimposed by non-ferroelectric phenomena, such as electrochemical deformation, electrostatic interactions, and/or charge injection.