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About

Jiankun Pu is a rising researcher at the forefront of computational materials science and electrochemical systems discovery. His work centers on accelerating the identification and design of novel materials for clean energy applications, with a particular focus on automating the traditionally slow, labor-intensive process of materials screening. Pu’s major contribution is the development of **AutoMat**, a pioneering automated platform for high-throughput computational discovery. In his 2022 paper, *AutoMat: Automated materials discovery for electrochemical systems* (19 citations), he demonstrated a powerful framework that integrates machine learning, density functional theory, and database mining to rapidly evaluate candidate materials for batteries, fuel cells, and electrocatalysts. This work builds on his earlier 2020 proof-of-concept study, which laid the groundwork for accelerating the search for electrochemical materials critical to large-scale electrification and decarbonizing the chemical industry. By significantly reducing the time from hypothesis to prediction, Pu’s research directly addresses key bottlenecks in the development of next-generation energy technologies, positioning him as an innovator in the emerging field of autonomous materials discovery.