Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

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 …

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 …

Heterogeneous electrocatalysis lies at the center of various technologies that could help
enable a sustainable future. However, its complexity makes it challenging to accurately and …

The science-informed design of 'green'carbonaceous materials (eg, biochar and activated
carbon) with high removal capacity of recalcitrant organic contaminants (eg …

This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …

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

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …

Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic
and electronic structures around the absorbing atom. However, the quantitative analysis of …