SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories
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.
Code (1)
Tasks
ArticlesBand GapLiterature MiningSimilar Papers 제목 키워드 기반
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerat…
AutoMLBayesian OptimizationFeature EngineeringModel Selection+1Multimodal Foundation Models for Material Property Prediction and Discovery
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recently, publicly available material data repo…
Property PredictionGraph Neural Network Prediction of Nonlinear Optical Properties
Nonlinear optical (NLO) materials for generating lasers via second harmonic generation (SHG) are highly sought in today's technology. However, discovering novel materials with considerable SHG is challenging due to the t…
Graph Neural NetworkPredictionInorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an a…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Word EmbeddingsThermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
New discoveries in chemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in ac…