Assessing different material properties to predict specific attributes, such as band gap, resistivity, young modulus, work function, and refractive index, is a fundamental requirement for materials science-based applications. However, the process is time-consuming and often requires extensive literature reviews and numerous experiments. Our study addresses these challenges by leveraging machine learning to analyze material properties with greater precision and efficiency. By automating the data extraction process and using the extracted information to train machine learning models, our developed model, SciQu, optimizes material properties. As a proof of concept, we predicted the refractive index of materials using data extracted from numerous research articles with SciQu, considering input descriptors such as space group, volume, and bandgap with Root Mean Square Error (RMSE) 0.068 and R2 0.94. Thus, SciQu not only predicts the properties of materials but also plays a key role in self-driving laboratories by optimizing the synthesis parameters to achieve precise shape, size, and phase of the materials subjected to the input parameters.

翻译：评估不同材料性能以预测特定属性（如带隙、电阻率、杨氏模量、功函数和折射率）是材料科学应用的基本要求。然而，该过程耗时且通常需要大量文献调研和重复实验。本研究通过机器学习技术以更高精度和效率分析材料性能，以应对这些挑战。通过自动化数据提取流程并利用提取信息训练机器学习模型，我们开发的SciQu模型能够优化材料性能。作为概念验证，我们使用SciQu从大量研究文献中提取数据预测材料折射率，所采用的输入描述符包括空间群、体积和带隙，其均方根误差为0.068，R²达到0.94。因此，SciQu不仅能预测材料性能，还能通过优化合成参数以获得符合输入参数要求的精确形状、尺寸和物相，从而在自主实验室中发挥关键作用。