Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in
chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of …

The benefits of using machine learning approaches in the design, optimisation and
understanding of homogeneous catalytic processes are being increasingly realised. We …

Metal–organic frameworks (MOFs) possess a virtually unlimited number of potential
structures. Although the latter enables an efficient route to control the structure-related …

The future of computational heterogeneous catalysis is shaped by machine learning in two
different but equally important areas:(i) development of atomistic potentials that closely …

Multicomponent reactions provide an excellent approach toward quaternary carbon centres
utilizing convergent chemical reactions in a highly selective manner under one‐pot …

Large language models (LLMs) are emerging as a powerful tool in chemistry across multiple
domains. In chemistry, LLMs are able to accurately predict properties, design new …

Mechanism-driven catalyst screening could be greatly accelerated by quantitative prediction
models of the reaction energy profile. Here, we propose a novel method for molecular …

Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively
studied for more than 50 years. Naively, one would expect that everything about this …

This paper presents a novel approach to predicting esterification procedures in organic
chemistry by employing generative large language models (LLMs) to interpret and translate …

Recent years have seen a rapid growth in the application of various machine learning
methods for reaction outcome prediction. Deep learning models have gained popularity due …