Machine Learning Accelerates Green Energy Material Discovery

By Sunil Sonkar
2 Min Read
Machine Learning Accelerates Green Energy Material Discovery

Scientists at Kyushu University, working with Osaka University and the Fine Ceramics Center, created a way to quickly find materials for eco-friendly tech using machine learning. They used a method to find and create two materials for devices called solid oxide fuel cells. These cells make energy without releasing carbon dioxide, using fuels like hydrogen. The finding is published in the Advanced Energy Materials journal.


To tackle climate change, scientists are creating ways to make energy without using fossil fuels. “One path to carbon neutrality is by creating a hydrogen society. However, as well as optimizing how hydrogen is made, stored and transported, we also need to boost the power-generating efficiency of hydrogen fuel cells,” explains Professor Yoshihiro Yamazaki, of Kyushu University’s Department of Materials Science and Technology, Platform of Inter-/Transdisciplinary Energy Research (Q-PIT).

To generate an electric current, solid oxide fuel cells need to be able to efficiently conduct hydrogen ions (or protons) through a solid material, known as an electrolyte. Currently, research into new electrolyte materials has focused on oxides with very specific crystal arrangements of atoms, known as a perovskite structure.

Professor Yamazaki explains the goal is to broaden the search for solid electrolytes beyond perovskite oxides, as they possess efficient proton-conducting capabilities, unlike perovskites which have been widely explored already.

However, discovering proton-conducting materials with alternative crystal structures via traditional “trial and error” methods has numerous limitations. For an electrolyte to gain the ability to conduct protons, small traces of another substance, known as a dopant, must be added to the base material. But with many promising base and dopant candidates – each with different atomic and electronic properties – finding the optimal combination that enhances proton conductivity becomes difficult and time-consuming.

Guided by these factors, the researchers then synthesized two promising materials, each with unique crystal structures, and assessed how well they conducted protons. Remarkably, both materials demonstrated proton conductivity in just a single experiment.

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