Smart Data-Driven GRU Predictor for SnO$_2$ Thin films Characteristics

Faiza Bouamra, Mohamed Sayah, Labib Sadek Terrissa, Noureddine Zerhouni
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Abstract

In material physics, characterization techniques are foremost crucial for obtaining the materials data regarding the physical properties as well as structural, electronics, magnetic, optic, dielectric, and spectroscopic characteristics. However, for many materials, ensuring availability and safe accessibility is not always easy and fully warranted. Moreover, the use of modeling and simulation techniques need a lot of theoretical knowledge, in addition of being associated to costly computation time and a great complexity deal. Thus, analyzing materials with different techniques for multiple samples simultaneously, still be very challenging for engineers and researchers. It is worth noting that although of being very risky, X-ray diffraction is the well known and widely used characterization technique which gathers data from structural properties of crystalline 1d, 2d or 3d materials. We propose in this paper, a Smart GRU for Gated Recurrent Unit model to forcast structural characteristics or properties of thin films of tin oxide SnO$_2$(110). Indeed, thin films samples are elaborated and managed experimentally and the collected data dictionary is then used to generate an AI -- Artificial Intelligence -- GRU model for the thin films of tin oxide SnO$_2$(110) structural property characterization.
智能数据驱动的 SnO$_2$ 薄膜特性 GRU 预测器
在材料物理学中,表征技术对于获取材料的物理性质以及结构、电子、磁性、光学、介电和光谱特性数据至关重要。然而,对于许多材料而言,确保其可用性和安全可得性并非易事,也并非完全有必要。此外,使用建模和模拟技术需要大量的理论知识,而且计算时间长、复杂度高。因此,对于工程师和研究人员来说,使用不同的技术同时分析材料的多个样本仍然是一项非常具有挑战性的工作。值得注意的是,X 射线衍射技术虽然风险很高,但却是众所周知、应用广泛的表征技术,它可以收集晶体 1d、2d 或 3d 材料的结构特性数据。我们在本文中提出了一种智能 GRU(即门控循环单元模型),用于预测氧化锡 SnO$_2$(110) 薄膜的结构特性或属性。事实上,我们通过实验对薄膜样品进行阐述和管理,然后利用收集到的数据字典生成用于氧化锡 SnO$_2$(110) 薄膜结构特性描述的 AI(人工智能)--GRU 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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