SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories

TL;DR

SciQu automates literature mining and machine learning to efficiently predict material properties, significantly reducing experimental efforts and aiding self-driving laboratories in materials synthesis optimization.

Contribution

This work introduces SciQu, a novel automated literature mining tool combined with machine learning for accurate and efficient prediction of material properties.

Findings

Predicted refractive index with RMSE 0.068 and R2 0.94

Automated data extraction improves prediction accuracy

Supports optimization of synthesis parameters in self-driving labs

Abstract

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.