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 …

The vastness of the materials design space makes it impractical to explore using traditional
brute-force methods, particularly in reticular chemistry. However, machine learning has …

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 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 …

Design of MOFs and intellectual content in reticular chemistry: a personal view - Chemical
Society Reviews (RSC Publishing) DOI:10.1039/B802802H Royal Society of Chemistry View …

The secondary building unit (SBU) approach was a turning point in the discovery of
permanently porous metal-organic frameworks (MOFs) and in launching the field of reticular …

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its
progress. Despite hidden variables and 'unknown unknowns' in datasets that may impede …

Whether we are pursuing chemical reactions or studying the properties of materials,
inevitably we seek to know and understand the geometrical aspects of chemical structures …

During the last decade, modern machine learning has found its way into synthetic chemistry.
Some long‐standing challenges, such as computer‐aided synthesis planning (CASP), have …

I was recently asked by a science reporter whether I am an organic or inorganic chemist. I
found myself saying that I am a reticular chemist. The reporter responded: I really like that …