AI Models For Materials That Could Harvest Electricity From Wasted Heat


A researcher from the University of Alberta developed two machine learning algorithms to find out the right material that could harvest electricity from waste heat efficiently. 

In the process of energy conversion, some amount of heat is generated. Even in green energy sources such as solar panels, extra heat is generated. But with up to 72 percent of it left unused, there’s also great potential to harvest electricity from that waste.

Researchers from University of Alberta have developed a way to figure out the chemistry behind that process. Alexander Gzyl, who conducted the research to earn his master’s degree in the Faculty of Science, developed two machine learning models to narrow down the chemical makeup of a group of alloys that could be used to create those materials.

“If we are able to turn the heat into something usable like electricity, we can make improvements to energy efficiency on a global scale,” noted Gzyl.

Gzyl worked with half-Heusler alloys, which are potential materials due to their stability, mechanical strength and efficiency. However, they still pose a challenge due to their specific chemical makeup.

“They are crystalline materials made up of certain chemical elements in a 1:1:1 ratio arranged in a specific way, but with more than 100,000 possible combinations of chemical elements in that ratio, only a fraction of all combinations results in the desired half-Heusler arrangement.”

With the help of two machine learning algorithms he developed, he was able to screen more than 300,000 simulation possibilities and narrow the field to just 103 candidates. That resulted in a list of new half-Heusler compounds and a way to determine their correct arrangement.

“Normally it could take up to 10 years to discover some new material,” Gzyl said. “Machine learning really streamlines that approach, and in this case we were able to test it out, take it beyond the theory into the real world, and it works.”

Gzyl believes his work could ultimately help speed up development of thermoelectric materials.



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