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

In the rapidly evolving landscape of electrochemical energy storage (EES), the advent of
artificial intelligence (AI) has emerged as a keystone for innovation in material design …

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs)
for predicting the synthesizability of inorganic compounds and the selection of precursors …

Immobilization of cadmium (Cd) in soil is a complex systematic process in which biochar
materials and experimental conditions need to be selected from an infinite space of …

Zeolites are complex and porous crystalline inorganic materials that serve as hosts for a
variety of molecular, ionic and cluster species. Formal, machine-actionable representation of …

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall
nanoparticles with molecule-like properties, bridging conventional metal–ligand complexes …

According to the fluorescence internal filtering effect (IFE), the more the absorption spectrum
of the quencher overlaps with the excitation and emission spectra of the fluorescent …

In the quest for sustainable energy solutions, the optimization of the photoelectrochemical
(PEC) performance of hematite photoanodes through cocatalysts represents a promising …

The advent of chatbots raises the possibility of a paradigm shift across society including the
most technical of fields with regard to access to information, generation of knowledge, and …

There is a significant potential for coding skills to transition fully to natural language in the
future. In this context, large language models (LLMs) have shown impressive natural …