발표연사 ▶▶▶

부문 Track
성명 Byungchan Han, Assistant Professor
소속 Yonsei University
주제 First-Principles Database Driven Computational Neural Network Approach to the Discovery of Active Ternary Nanocatalysts for Oxygen Reduction Reaction
학력 Ph.D. Materials Science & Engineering, MIT (2007)
M.S. Nuclear Engineering, Seoul National University (2000)
B.S., Nuclear Engineering, Seoul National University (1998)
경력 Associate Professor, Yonsei University (2015 – Current)
Assistant & Associate Professor, DGIST (2011-2014)
Postdoctoral Associate, MIT (2007-2009), Stanford University (2009-2011)
요약 An elegant machine-learning-based algorithm is applied to study thermo- electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR).
A high-dimensional neural network potentials (NNPs) for the interactions among the components are parameterized from big data set established by first-principles density functional theory calculations.
The NNPs are, then, incorporated with Monte Carlo (MC) and molecular dynamics (MD) simulations to identify not only active but also electrochemically stable nanocatalyst for ORR in acidic solution. Effects of surface strain caused by selective segregation of certain components on the catalytic performance is accurately characterized.
The computationally efficient and precise approach proposes a promising ORR candidate: 2.6nm icosahedron consisting of 60 % of Pt and Ni/Cu for the rest.
Our methodology can be applied for high-throughput screening and designing of key functional nanomaterial to dramatically enhance the performance of various electrochemical system.