About us

Catalyst informatics is proposed through the integration of experimental, computational, and data sciences with the objectives of discovering novel, innovative catalysts and elucidating the catalytic reaction mechanisms. This research is executed through catalyst database construction, transformation of data to knowledge through machine learning, and platform design, which all act as the pillars of catalytic informatics. In particular, the methane conversion process is targeted where innovative catalysts for methane conversion are explored and novel methane conversion processes are proposed using catalyst informatics. This research thus revolutionizes how novel catalysts are discovered and designed.

News & Topics

2019.6.18
Automatic recognition of oxidation sate in XAFS spectra via machine learning “Automatic oxidation threshold recognition of XAFS data using supervised machine learning" is accepted in Molecular Systems Design & Engineering
2019.1.4
Prediction of crystal structures via machine learning and materials data “Creating Machine Learning-Driven Material Recipes Based on Crystal Structure" is accepted in The Journal of Physical Chemistry Letters.
2018.12.17
Concept article “The Rise of Catalyst Informatics: Towards Catalyst Genomics" is accepted in ChemCatChem.
2018.8.3
Catalysts and Materials database construction via ontology “Redesigning the Materials and Catalysts Database Construction Process Using Ontologies" is accepted in Journal of Chemical Information and Modeling.
2018.4.23
Prediction of Oxidative coupling of methane catalysts using Catalyst Informatic “Unveiling hidden catalysts for the oxidative coupling of methane based on combining machine learning and literature" is published in ChemCatChem.
2017.10.1
The research project entitled "Discovery of methane conversion catalysts and revealing reaction mechanisms through the integration of experimental, computational, and data sciences" has been accepted by the section "Innovative catalysts and creation technologies for the utilization of diverse natural carbon resources" of the CREST funding program.