Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

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 …

Covalent organic frameworks (COFs) are a class of organic crystalline porous materials
discovered in the early 21st century that have become an attractive class of emerging …

Transition metal-catalysed direct borylation of hydrocarbons via C–H bond activation has
received a remarkable level of attention as a popular reaction in the synthesis of …

Metal–organic frameworks (MOFs) have captivated researchers for over 25 years, yet few
have successfully transitioned to commercial markets. This Perspective elucidates the …

The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an
almost boundless number of materials some of which can be a substitute for the traditionally …

We present a new framework integrating the AI model GPT‐4 into the iterative process of
reticular chemistry experimentation, leveraging a cooperative workflow of interaction …

Metal–organic framework (MOF) materials are gaining significant interest in biomedical
research, owing to their high porosity, crystallinity, and structural and compositional diversity …

We leveraged the power of ChatGPT and Bayesian optimization in the development of a
multi-AI-driven system, backed by seven large language model-based assistants and …