JK

Jaehyun Kim, Ph.D.

Research

Research directions

My work spans several directions across computational and experimental electrochemistry — from ML-guided catalyst design to operando mechanism and device-scale interfaces. The tungsten-single-atom study further down is one worked example.

Career & Education

Career & education

Download CV (PDF) →

Journey
Selected Projects
Core Expertise
Flagship study · Nature Communications 2026

ML-guided tungsten single atoms for noble-metal-free water electrolysis

One worked example of the directions above. In this first-author study, I fine-tuned EquiformerV2 on DFT data, screened the configuration space, and identified W₁-NiFeOOH. I synthesized it by cyclic electrodeposition, characterized it with operando Raman and synchrotron XAS, and tested it in an anion-exchange-membrane electrolyzer.

13.1 A cm⁻²
noble-metal-free AEM electrolysis @ 2.0 V
3,976
single-atom configurations ML-screened
~1,000×
faster screening than DFT (EquiformerV2)
Machine learning-guided catalyst screening (Nature Communications 2026, Fig. 1)
Fig. 1 — ML-guided catalyst screening: EquiformerV2 + DFT, OER activity volcano, and raw-material cost analysis. Nature Communications 17, 2344 (2026).
Computation → device, in this study
Publications

Selected publications

A few highlights below · full record on the publications page and Google Scholar. 1st / co-1st = first or co-first author.

View all publications →

At a glance
1,574
citations · h-index 23 (Google Scholar)
39
peer-reviewed SCI(E) papers
12
first / co-first author
1
patent (pending) · 5 talks
News
Biography

Contact