  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.
