https://sj.jst.go.jp/news/202602/n0206-02k.html

https://pubs.acs.org/doi/10.1021/acscatal.5c06298

A research group at Hokkaido University has developed a knowledge system called “ChemOntology”, a computational framework that extracts and applies chemical knowledge from reaction intermediates generated during automated reaction path searches. By integrating chemical and geometric knowledge generated using chemical ontology and topology, it identifies chemically relevant reaction paths and geometries to guide the reaction path search.

ChemOntology relies on three main inputs: the reaction setup, chemically informed assumptions encoded as Elementary Reaction Process Ontologies (ERPOs), and a set of reaction rules. Together, these enable efficient use of extracted knowledge to accelerate reaction exploration. Unlike machine learning models, it requires no training on data sets and is broadly applicable to a wide range of organometallic systems, offering a robust tool for mechanistic analysis and rational catalyst design, especially in systems where human insight remains indispensable.