(PRESS RELEASE) Machine learning method speeds up discovery of green energy materials
A group of researchers from Kyushu University, led by Professor Yoshihiro Yamazaki from the Department of Materials Science and Technology, Platform of Inter-/Transdisciplinary Energy Research (Q-PIT), in collaboration with Osaka University and the Fine Ceramics Center, have developed a machine learning framework to expedite the discovery of materials needed for green energy technology. Using this new approach, the team has identified and synthesized two new candidate materials for solid oxide fuel cells (devices that generate energy using fuels like hydrogen and do not emit carbon dioxide in the process).
The discovery was published in the Advanced Energy Materials journal and has implications beyond the energy sector, as it can speed up the search for other innovative materials.
Research result
The full version of the press release in English is available on the Kyushu University website and can be found here.
Research paper information
Journal: Advanced Energy Materials, 2301892, 2023
Title: Discovery of Unconventional Proton-Conducting Inorganic Solids via Defect-Chemistry-Trained, Interpretable Machine Learning
Authors: Susumu Fujii, Yuta Shimizu, Junji Hyodo, Akihide Kuwabara*, and Yoshihiro Yamazaki*
DOI:10.1002/aenm.202301892