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Home AI Next

The Use Of Machine Learning Accelerates The Search For Renewable Energy Sources

January 19, 2024
machine learning

As the planet warms, scientists are creating novel methods of producing energy without the use of fossil fuels.

“Building a hydrogen society is one way to achieve carbon neutrality. But we also need to increase the power-generating efficiency of hydrogen fuel cells in addition to streamlining the production, storage, and transportation of hydrogen,” says Professor Yoshihiro Yamazaki of Kyushu University’s Department of Materials Science and Technology, Platform of Inter-/Transdisciplinary Energy Research (Q-PIT).

Solid oxide fuel cells must be able to effectively conduct hydrogen ions, also known as protons, through a solid substance called an electrolyte in order to produce an electric current.

Currently, oxides with extremely particular atom crystal arrangements—known as perovskite structures—are the subject of research into novel electrolyte materials.

“The first proton-conducting oxide discovered was in a perovskite structure, and new high-performing perovskites are continually being reported,” claims Professor Yamazaki.

“But we want to expand the discovery of solid electrolytes to non-perovskite oxides, which also have the capability of conducting protons very efficiently.”

However, using conventional “trial and error” approaches to find proton-conducting materials with different crystal structures has many drawbacks.

Tiny amounts of an additional material, referred to as a dopant, must be added to the base material in order for an electrolyte to acquire the capacity to carry protons.

But with so many interesting candidates for bases and dopants, each possessing unique atomic and electronic properties, it becomes challenging and time-consuming to identify the best combination to increase proton conductivity.

Rather, the scientists computed the characteristics of several oxides and dopants.

They then analyzed the data, determined the variables influencing a material’s proton conductivity, and made predictions about possible combinations using machine learning.

The scientists then created two intriguing materials, each with a distinct crystal structure, and evaluated how well they carried protons, all under the guidance of these variables.

Notably, in a single experiment, both materials showed proton conductivity.

The first known proton conductor with a sillenite crystal structure is one of the materials, the researchers noted.

The high-speed proton conduction path of the other, with a eulytite structure, is different from the conduction paths observed in perovskites.

These oxides don’t work well as electrolytes at the moment, but the research team thinks that if they are studied more, they will become more conductible.

“Our approach has the potential to dramatically expedite the development of solid oxide fuel cells by broadening the search field for proton-conducting oxides. Professor Yamazaki says, “It’s a promising step toward realizing a hydrogen society.” “With minor modifications, this framework could also be adapted to other fields of materials science, and potentially accelerate the development of many innovative materials.”

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