Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Coupled electrochemical systems for the direct capture and conversion of CO 2 have
garnered significant attention owing to their potential to enhance energy-and cost-efficiency …

Materials language processing (MLP) can facilitate materials science research by
automating the extraction of structured data from research papers. Despite the existence of …

Information extraction and textual comprehension from materials literature are vital for
develo** an exhaustive knowledge base that enables accelerated materials discovery …

Structural characterization of polymer materials is a major step in the process of creating
complex materials design-structural-property relationships. With growing interests in artificial …

We present a simulation of various active learning strategies for the discovery of polymer
solar cell donor/acceptor pairs using data extracted from the literature spanning∼ 20 years …

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 …

A key challenge in artificial intelligence (AI) is the creation of systems capable of
autonomously advancing scientific understanding by exploring novel domains, identifying …

Dielectric capacitors, characterized by ultra-high power densities, have been widely used in
Internet of Everything terminals and vigorously developed to improve their energy storage …