用GRU模型预测SnO₂薄膜结构特性，省去复杂实验与计算。
Smart Data-Driven GRU Predictor for SnO$_2$ Thin films Characteristics
- 基于GRU的智能模型，从实验数据中学习薄膜结构特征。
- 可准确预测SnO₂(110)薄膜的晶格参数等关键结构信息。
- 适合材料工程师快速评估薄膜性能，减少实验成本。
在材料物理中，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，确保数据的可用性和安全访问并不总是容易实现。此外，建模与仿真技术需要大量理论知识，且伴随高昂的计算成本和复杂性。因此，同时使用多种技术对多个样品进行分析，对工程师和研究人员仍极具挑战。值得注意的是，尽管存在风险，X射线衍射仍是广泛使用的表征技术，可获取一维、二维或三维晶体材料的结构数据。本文提出一种智能门控循环单元（GRU）模型，用于预测二氧化锡（SnO₂，110）薄膜的结构特性。实验制备了多组SnO₂薄膜样品，收集其数据后构建人工智能GRU模型，实现对薄膜结构特性的高效表征。
原文摘要 · Abstract (English)
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.
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