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Research result

(PRESS RELEASE) Machine learning method speeds up discovery of green energy materials

2024/01/22

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

Through the use of advanced materials science simulations and machine learning models, the research team has identified suitable combinations of base compounds and dopants to develop guidelines for introducing protons into materials. Using the developed guidelines, the team successfully synthesized Pb-doped Bi12SiO20 and Sr-doped Bi4Ge3O12 in a single attempt, and both were found to be new proton-conducting oxides (as shown in figure above). The former is particularly noteworthy as it's the first proton-conducting oxide in the world to have a sillenite structure and only consist of solely the group 14 and 15 cations. This is a significant breakthrough in the quest for searching better proton conductors.

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

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