Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …

Abstract “Artificial Intelligence” in all its forms has emerged as a transformative technology
that is in the process of resha** many aspects of industry and wider society at a global …

The ever-growing abundance of data found in heterogeneous sources, such as scientific
publications, has forced the development of automated techniques for data extraction. While …

Deep learning-based object detection models have recently found widespread use in
materials science, with rapid progress made in just the past two years. Scanning and …

Recent advancements in artificial intelligence and automation are transforming catalyst
discovery and design from traditional trial-and-error manual mode into intelligent, high …

Materials research studies are dealing with a large number of images, which can now be
facilitated via image-based machine learning techniques. In this article, we review recent …

The vast majority of materials science knowledge exists in unstructured natural language,
yet structured data is crucial for innovative and systematic materials design. Traditionally, the …

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to
reveal predictive structure–property relationships. For many properties of interest in …

Most of the knowledge in materials science literature is in the form of unstructured data such
as text and images. Here, we present a framework employing natural language processing …