김경덕 교수

Discovering novel, multicomponent crystalline materials is a complex task owing to the large space of feasible structures. Here we demonstrate a method to significantly accelerate materials discovery by using a machine learning (ML) model trained on density functional theory (DFT) data from the Open Quantum Materials Database (OQMD). Our ML model predicts the stability of a material based on its crystal structure and chemical composition, and we illustrate the effectiveness of the method by appl

The kinetics of oxidation is examined using a phase-field model of electrochemistry when the oxide film is smaller than the Debye length. As a test of the model, the phase-field approach recovers the results of classical Wagner diffusion-controlled oxide growth when the interfacial mobility of the oxide-metal interface is large and the films are much thicker than the Debye length. However, for small interfacial mobilities, where the growth is reaction controlled, we find that the film increases

Copper (Cu) is a catalyst broadly used in industry for hydrogenation of carbon dioxide, which has broad implications for environmental sustainability. An accurate understanding of the degeneration behavior of Cu catalysts under <i>operando</i> conditions is critical for uncovering the failure mechanism of catalysts and designing novel ones with optimized performance. Despite the widespread use of these materials, their failure mechanisms are not well understood because conventional characterizat