arXiv subjects

Jung-Hae Choi

Publications and source records attributed to Jung-Hae Choi.

6 recordsLinked to original sources

arXiv2026

VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations

Multiscale modeling, which integrates material properties from ab initio calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while ab initio methods provide diffusion parameters specific to individual migration paths, continuum equations require a single effective set of parameters that captures the overall diffusion behavior. To address this issue, we present VacHopPy, an open-source Python package for vacancy hopping analysis based on molecular dynamics (MD). VacHopPy extracts an effective set of hopping parameters, including hopping distance, hopping barrier, number of effective paths, correlation factor, and attempt frequency, by statistically integrating energetic, kinetic, and geometric contributions across all paths. It also includes tools for tracking vacancy trajectories and for detecting phase transitions during MD simulations. The applicability of VacHopPy is demonstrated in three representative materials: face-centered cubic Al, rutile TiO2, and monoclinic HfO2. The extracted effective parameters reproduce temperature-dependent diffusion behavior and are in good agreement with previous experimental data. Provided in a simplified form, these parameters are well suited for continuum-scale models and remain valid over a wide temperature range spanning several hundred kelvins. Furthermore, VacHopPy inherently accounts for anisotropy in thermal vibrations, a factor often overlooked, making it suitable for simulating diffusion in complex crystals. Overall, VacHopPy establishes a robust bridge between atomic- and continuum-scale models, enabling more reliable multiscale simulation

cond-mat.mtrl-sci↗

arXiv2021

InterPhon: Ab initio Interface Phonon Calculations within a 3D Electronic Structure Framework

This work provides the community with an easily executable open-source Python package designed to automize the evaluation of Interfacial Phonons (InterPhon). Its strategy of arbitrarily defining the interfacial region and periodicity alleviates the excessive computational cost in applying ab initio phonon calculations to interfaces and enables efficient extraction of interfacial phonons. InterPhon makes it possible to apply all of the phonon-based predictions that have been available for bulk systems, to interfacial systems. The first example, in which this package was applied to InAs surfaces, demonstrates a systematic structure search for unexplored surface reconstructions, navigated by the imaginary mode of surface phonons. It eventually explains the anisotropic surface vibrations of the polar crystal. The second example, involving oxygen adsorption on Cu, reveals adsorption-induced vibrational change and its contribution to energetic stability. The third example, on a Si/GaAs interface, shows distinct vibrational patterns depending on interfacial structures. It leads to a prediction regarding the structural transition of interfaces and unveils the processing conditions for spontaneous growth of GaAs nanowires on Si. High-level automation in InterPhon will be of great help in elucidating interfacial atomic dynamics and in implementing an automated computational workflow for diverse interfacial systems.

cond-mat.mtrl-sci↗

arXiv2020

Influence of surface structure and strain on indium segregation in InGaAs: a first-principles investigation

The surface segregation of indium atoms in InGaAs is investigated using first-principles calculations based on density functional theory. Through the calculation of segregation energies for (100), (110), and (111) surfaces of GaAs we analyze the decisive role of surface orientation on indium segregation. Further, our calculations reveal that the variation of segregation energy as a function of applied strain strongly depends on surface reconstruction. Obtained segregation energy trends are discussed in light of atomic bonding probed via integrated crystal orbital Hamilton population. Results presented in this paper are anticipated to guide experimental efforts to achieve control on In segregation by managing As-flux along with the application of strain.

cond-mat.mtrl-sci↗

arXiv2020

AMMCR: Ab-initio model for mobility and conductivity calculation by using Rode Algorithm

We present a module to calculate the mobility and conductivity of semi-conducting materials using Rode's algorithm. This module uses a variety of electronic structure inputs derived from the Density Functional Theory (DFT). We have demonstrated good agreement with experimental results for the case of Cadmium Sulfide (CdS). We also provide a comparison with the widely used method, the so called Relaxation Time Approximation (RTA) and demonstrated the improvisation of the results compared to RTA. The present version of the module is interfaced with Vienna ab-initio simulation package (VASP).

cond-mat.mtrl-sci↗

arXiv2019

Error Minimization in Predicting Accurate Adsorption Energies Using Machine Learning

Finding the "ideal" catalyst is a matter of great interest in the communities of chemists and material scientists, partly because of its wide spectrum of industrial applications. Information regarding a physical parameter termed "adsorption energy", which dictates the degrees of adhesion of an adsorbate on a substrate is a primary requirement in selecting the catalyst for catalytic reactions. Both experiments and \textit{in-silico} modelling are extensively being used in estimating the adsorption energies, both of which are \textit{Edisonian} approach and demands plenty of resources and are time consuming. In this report, by employing a data centric approach almost instantly we predict the adsorption energies of atomic and molecular gases on the surfaces of many transition metals (TMs). With less than 10 sets of simple atomic features, our predictions of the adsorption energies are within a root-mean-squared-error (RMSE) of less than 0.4 eV with the quantum many-body perturbation theory estimates, a computationally expensive with good experimental agreement. Further, we minimized the RMSE up to 0.11 eV by using the precomputed adsorption energies obtained with conventional exchange and correlation (XC) functional as one component of the feature vector. Based on our results, we developed a set of scaling laws between the adsorption energies computed with many-body perturbation theory and conventional DFT XC-functionals.

cond-mat.mtrl-sci↗

arXiv2012

Monte Carlo simulations of the structure of Pt-based bimetallic nanoparticles

Pt-based bimetallic nanoparticles have attracted significant attention as a promising replacement for expensive Pt nanoparticles. In the systematic design of bimetallic nanoparticles, it is important to understand their preferred atomic structures. However, compared with unary systems, alloy nanoparticles present more structural complexity with various compositional configurations, such as mixed-alloy, core-shell, and multishell structures. In this paper, we developed a unified empirical potential model for various Pt-based binary alloys, such as Pd-Pt, Cu-Pt, Au-Pt, and Ag-Pt. Within this framework, we performed a series of Monte Carlo (MC) simulations that quantify the energetically favorable atomic arrangements of Pt-based alloy nanoparticles: an intermetallic compound structure for the Pd-Pt alloy, an onion-like multi-shell structure for the Cu-Pt alloy, and core-shell structures (Au@Pt and Ag@Pt) for the Au-Pt and Ag-Pt alloys. The equilibrium nanoparticle structures for the four alloy types were compared with each other, and the structural features can be interpreted by the interplay of their material properties, such as the surface energy and heat of formation.

cond-mat.mtrl-sci↗