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.

翻译：在材料物理学中，表征技术对于获取材料在物理性质以及结构、电子、磁学、光学、介电和光谱特性方面的数据至关重要。然而，对于许多材料而言，确保数据的可获得性与安全访问并非总是容易且完全有保障的。此外，建模与仿真技术的使用除了涉及高昂的计算时间和极大的复杂性外，还需要大量的理论知识。因此，同时使用多种技术分析多个样品，对工程师和研究人员而言仍然极具挑战性。值得注意的是，尽管存在较高风险，X射线衍射仍是众所周知且广泛使用的表征技术，它可收集一维、二维或三维晶体材料的结构特性数据。本文提出一种智能门控循环单元模型，用于预测氧化锡SnO$_2$(110)薄膜的结构特性或性质。具体而言，我们通过实验制备并处理薄膜样品，随后利用收集的数据字典构建一个人工智能GRU模型，以实现对氧化锡SnO$_2$(110)薄膜结构特性的表征。