arXiv subjects

Hyeon Woo Kim

Publications and source records attributed to Hyeon Woo Kim.

4 recordsLinked to original sources

arXiv2026

Oxygen stoichiometry directs rutile-anatase phase selection through kinetic control of nucleation

Synthesis of a target polymorph remains more empirical than predictive because crystallization often selects the most accessible nucleation pathway rather than the thermodynamically most stable phase. Here, we show that oxygen stoichiometry converts this empirical synthesis variable into a kinetic control parameter for anatase-rutile selection in TiO$_{2-x}$. Enhanced-sampling simulations reveal that oxygen content alters the nucleation-barrier landscape, switching the relative accessibility of anatase and rutile, even while rutile remains thermodynamically favored. Molecular dynamics simulations show the presence of a diffuse intermediate shell around the nucleus, where oxygen deficiency alters Ti-O coordination and connectivity and drives shell-local motif evolution from anatase-like toward rutile-like environments. A coupled-flux model that integrates barrier competition with shell-mediated attachment/exchange yields a relative nucleation-rate map consistent with reported oxygen-dependent synthesis trends. These results establish stoichiometry-controlled intermediate-shell motif evolution as a kinetic origin of polymorph selection and provide a framework for predicting target phases in composition-coupled crystallization.

cond-mat.mtrl-sci↗

arXiv2026

Precursor-Dependent Energetics as a Predictive Principle for Polymorph Selection in Thin Films

Vapor deposition allows for the synthesis of metastable polymorphs with unique properties, yet polymorph selection remains largely empirical due to the lack of predictive guidelines bridging thermodynamics, kinetics, and synthesis conditions. Here, we show that precursor chemistry can control metastable polymorph selection by modulating the reaction driving force governing nucleation. By integrating first-principles reaction energetics and substrate interactions into classical nucleation theory, we establish a quantitative framework that connects precursor-dependent reaction energetics to polymorph accessibility during vapor deposition. Using Ga2O3 as a model system, we demonstrate that highly reactive precursors with large reaction driving forces kinetically stabilize the metastable α phase, whereas low-driving-force precursors permit thermodynamic relaxation to the stable \b{eta} phase. Furthermore, precursor flow rates amplify supersaturation, expanding the kinetic window for stabilizing the elusive \k{appa} phase. The predictive capability of this approach is further validated in the TiO2 system, where precursor-dependent reaction energetics correctly capture the competitive nucleation between rutile and anatase. These results establish precursor chemistry as a tunable chemical lever for controlling nucleation kinetics and provide a predictive design principle for metastable polymorph synthesis in vapor deposition.

cond-mat.mtrl-sci↗

arXiv2024

Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

cond-mat.mtrl-sci↗

arXiv2023

Designing Pr-based Advanced Photoluminescent Materials using Machine Learning and Density Functional Theory

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr$_3$AlO$_6$, Pr$_4$Al$_2$O$_9$, Pr$_3$ScO$_6$ and Pr$_3$Sc$_5$O$_{12}$) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.

cond-mat.mtrl-sci↗