Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

To close the gap between the rates of computational screening and experimental realization
of novel materials,, we introduce the A-Lab, an autonomous laboratory for the solid-state …

We use prompt engineering to guide ChatGPT in the automation of text mining of metal–
organic framework (MOF) synthesis conditions from diverse formats and styles of the …

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 …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Materials science and chemistry have played a central and significant role in advancing
society. With the shift toward sustainable living, it is anticipated that the development of …

Synthesis prediction is a key accelerator for the rapid design of advanced materials.
However, determining synthesis variables such as the choice of precursor materials is …