Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The design of heterogeneous catalysts relies on understanding the fundamental surface
kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful
computational method for fundamental research in science branches such as biology …

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …

Electrochemical and photoelectrochemical CO2 reduction technologies offer the promise of
zero-carbon-emission renewable fuels needed for heavy-duty transportation. However, the …

Accurate in silico prediction of protein–ligand binding affinities has been a primary objective
of structure-based drug design for decades due to the putative value it would bring to the …

Solubility prediction remains a critical challenge in drug development, synthetic route and
chemical process design, extraction and crystallisation. Here we report a successful …

An automated and broadly applicable workflow for the description of solvation effects in an
explicit manner is introduced. This method, termed quantum cluster growth (QCG), is based …

With high scalability and independent control over energy and power, redox flow batteries
(RFBs) stand out as an important large-scale energy storage system. However, the …