The paper of Yin Kan Phua (2nd term fellow) entitled “Unsupervised Machine Learning-Derived Anion-Exchange Membrane Polymers Map: A Guideline for Polymers Exploration and Design” was published in ChemElectroChem journal.

The study introduces a novel anion exchange membrane materials (a critical component in fuel cells and water electrolyzers) map generated using unsupervised machine learning. By leveraging chemical structural information, this map successfully unravels the intricate relationship between anion conductivity and the characteristics of various anion exchange membranes. Researchers can significantly enhance material design efficiency by combining their domain expertise with insights from this comprehensive materials map. Moreover, the versatility of the methodology holds great promise for accelerating the development of a wide range of materials.

The press release about the achievement was posted on the Kyushu University website and can be found here (Japanese).

Research paper information
Journal: ChemElectroChem
Title: Unsupervised Machine Learning-Derived Anion-Exchange Membrane Polymers Map: A Guideline for Polymers Exploration and Design
Authors: Yin Kan Phua, Nana Terasoba, Manabu Tanaka, Tsuyohiko Fujigaya, Koichiro Kato
DOI: 10.1002/celc.202400252