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

TL;DR

This paper introduces a smart GRU-based AI model to predict the structural properties of SnO$_2$ thin films, aiming to simplify and accelerate material characterization processes compared to traditional techniques.

Contribution

The paper presents a novel application of a GRU neural network for predicting thin film properties, reducing reliance on complex and costly experimental characterization methods.

Findings

The model accurately forecasts structural properties of SnO$_2$ thin films.

The approach reduces the need for extensive experimental testing.

It demonstrates potential for efficient material analysis in physics research.

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.