Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

Intelligently extracting and linking complex scientific information from unstructured text is a
challenging endeavor particularly for those inexperienced with natural language processing …

Massive scale, in terms of both data availability and computation, enables important
breakthroughs in key application areas of deep learning such as natural language …

Gold nanoparticles are highly desired for a range of technological applications due to their
tunable properties, which are dictated by the size and shape of the constituent particles …

Learned representations of scientific documents can serve as valuable input features for
downstream tasks without further fine-tuning. However, existing benchmarks for evaluating …

In this perspective, we highlight results of a research consortium devoted to advancing
understanding of oxygen reduction reaction (ORR) catalysis as a means to inform fuel cell …

The ever-increasing number of materials science articles makes it hard to infer chemistry-
structure-property relations from literature. We used natural language processing methods to …

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across
numerous sectors. Large language models (LLMs) are an emerging subset technology of AI …