About Evan Kim
Evan used machine learning software to try to identify new superconductors. Compared to traditional “guess-and-check” techniques, which have an average success rate of just 3%, more than 70% of Evan’s new predicted compounds appear to be superconductor materials.
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Evan Kim, 17, of Redmond, used machine learning software to identify new hypothetical superconductors for his Regeneron Science Talent Search physics project. A superconductor is an element or alloy that can conduct electricity perfectly, without resistance. Still, to do so, it needs to be supercooled, usually to near minus 459 F. A few “high-temperature” superconductors (HTS) have been identified that are capable of working at temperatures as warm as minus 280 F, which is above the practical temperature of liquid nitrogen. Evan used a type of machine learning to mimic the characteristics of known superconductors in hopes of discovering similar elements and alloys. Previous attempts, which Evan refers to as “guess-and-check” techniques, had an average success rate of only 3%, but according to an established computer model, more than 70% of his newly predicted compounds appear to be superconductors, and at least one appears to be an HTS.
Evan attends Tesla STEM High School, where he is president of the physics and science bowl clubs. He also coaches a middle school math club and volunteers with the Online Physics Olympiad. Evan is the son of Justin Kim and Hyeweon Park.
Beyond the Project
Evan enjoys solving “beautiful problems” in Physics Olympiads and makes online videos to help fellow students learn how to solve complex problems even if they’re working alone.
FUN FACTS: Evan’s favorite hobby is GeoGuessr, where online players are put in random Google Street View locations and guess where they are on the globe. He prizes his virtual experiences in new parts of the world.