arXiv subjects

Dongwon Shin

Publications and source records attributed to Dongwon Shin.

17 recordsLinked to original sources

arXiv2026

Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution

Unambiguous identification of Majorana zero modes (MZMs) in topological superconductors (TSCs) remains a challenge due to complex in-gap states that can also produce zero-bias conductance peaks (ZBPs). Here we demonstrate a data-driven workflow that integrates pixel-wise spectral deconvolution with machine-learning (ML) to analyze tunneling spectroscopy from FeTe0.55Se0.45, an intrinsic TSC. Based on the local density of states (LDOS) spectra acquired with a millikelvin scanning tunneling microscope under magnetic fields, each spectrum was decomposed into multiple Lorentzian peaks. The extracted peak parameters were assembled into a structured feature set and subsequently embedded and clustered with unsupervised ML algorithms. ML-based clustering identified distinct classes of LDOS spectra, separating superconductor vortices exhibiting ZBPs consistent with established characteristics of MZMs from vortices displaying ZBP-mimicking features of trivial origin. Furthermore, spatially resolved ZBP distributions differentiate isotropic vortex cores with well-defined ZBPs from vortices that exhibit locally distorted ZBPs. By comparing the ZBP distributions to defect locations measured without magnetic field, we found a correlation between local heterogeneity and the ZBP formation, necessitating the systematic, data-driven analysis to disentangle genuine MZM signatures in TSC. This objective and reproducible workflow advances reliable MZM detection in TSCs, providing a foundation for MZM manipulation towards quantum computation.

cond-mat.supr-con↗

arXiv2025

Improving 2D-ness to enhance thermopower in oxide superlattices

The transport dynamics of itinerant charge carriers and their interactions with the environment. For two-dimensional oxide thermoelectrics, predominantly represented by doped SrTiO3-based superlattices, reduced spatial dimensions and increased effective mass are known to enhance thermopower (S). However, because of their large effective Bohr radius resulting from their high dielectric constant, SrTiO3-based systems have limitations in exhibiting the 2D characteristic. Here, we focus on EuTiO3 as an alternative perovskite platform in which fractional LaxEu1-xTiO3/EuTiO3 artificial superlattices demonstrate the improvement in 2D nature for the dimensionality-induced improvement of S. We observed a quasi-2D thermopower S2D of -950 uV K-1 and S2D/S3D of ~20 resulting from the improved 2D confinement. Thermopower measurements, combined with hybrid density functional theory calculations, show the enhanced S originates from the confinement of Ti 3dxy-states within the LaxEu1-xTiO3 layers and the associated increase in the 2D density of states. In detail, a smaller effective Bohr radius and modified electronic band structures, in conjunction with the presence of the Eu 4f-states in EuTiO3 which modified the local electronic potential and strengthened the spatial confinement of Ti 3d-states. This approach to improving the dimensional confinement establishes a small effective Bohr radius and Eu 4f-state assisted 2D confinement provides valuable insights into the design of high-performance applications in artificial oxide superlattices.

cond-mat.mtrl-sci↗

arXiv2025

Universal Fabrication of Graphene/Perovskite Oxide Hybrid Heterostructures

Hybrid heterostructures composed of graphene and perovskite oxides provide a promising platform for exploiting synergetic interfacial functionalities. Conventional fabrication methods of the hybrid heterostructures rely on transferring graphene grown on metallic substrates-- a process that is time-consuming, labor-intensive, and prone to introducing numerous defects. In this study, we present a universal, catalyst-free method for the direct growth of graphene on insulating substrates by employing three different perovskite oxide substrates (SrTiO$_3$, LaAlO$_3$, and (La$_{0.18}$Sr$_{0.82}$)(Al$_{0.59}$Ta$_{0.41}$)O$_3$) using atmospheric chemical vapor deposition. Comprehensive characterization via Raman spectroscopy, X-ray spectroscopy, scanning probe microscopy, and electron microscopy confirmed the formation of a uniform, continuous monolayer graphene on all substrates. We identified that growth temperature critically governs graphene quality, as excessive active species may lead to secondary nucleation and the formation of multilayer graphene. Notably, all substrates shared the same optimal growth conditions. Low-temperature Raman spectroscopy and scanning tunneling microscopy of the graphene/SrTiO$_3$ hybrid heterostructure revealed cooperative phenomena, including substrate-induced lattice-phonon and electron-phonon coupling. Our work establishes a reproducible, transfer-free fabrication route for graphene/perovskite oxide hybrid heterostructures and provides empirical support for the universal growth of graphene on insulating substrates.

cond-mat.mtrl-sci↗

arXiv2024

Electrically Tunable Spin Exchange Splitting in Graphene Hybrid Heterostructure

Graphene, with spin and valley degrees of freedom, fosters unexpected physical and chemical properties for the realization of next-generation quantum devices. However, the spin symmetry of graphene is rather robustly protected, hampering manipulation of the spin degrees of freedom for the application of spintronic devices such as electric gate tunable spin filters. We demonstrate that a hybrid heterostructure composed of graphene and LaCoO3 epitaxial thin film exhibits an electrically tunable spin exchange splitting. The large and adjustable spin exchange splitting of 155.9 - 306.5 meV was obtained by the characteristic shifts in both the spin symmetry broken quantum Hall states and the Shubnikov-de-Haas oscillations. Strong hybridization induced charge transfer across the hybrid heterointerface has been identified for the observed spin exchange splitting. The substantial and facile controllability of the spin exchange splitting provides an opportunity for spintronics applications with the electrically-tunable spin polarization in hybrid heterostructures.

cond-mat.str-el↗

arXiv2023

Application of Machine Learning to Sporadic Experimental Data for Understanding Epitaxial Strain Relaxation

Understanding epitaxial strain relaxation is one of the key challenges in functional thin films with strong structure-property relation. Herein, we employ an emerging data analytics approach to quantitatively evaluate the underlying relationships between critical thickness (hc) of strain relaxation and various physical and chemical features, despite the sporadic experimental data points available. First, we have collected and refined reported hc of perovskite oxide thin film/substrate system to construct a consistent sub-dataset which captures a common trend among the varying experimental details. Then, we employ correlation analyses and feature engineering to find the most relevant feature set which include Poisson's ratio and lattice mismatch. With the insight offered by correlation analyses and feature engineering, machine learning (ML) models have been trained to deduce a decent accuracy, which has been further validated experimentally. the demonstrated framework is expected to be efficiently extended to the other classes of thin films in understanding hc.

cond-mat.mtrl-sci↗

arXiv2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

cond-mat.mtrl-sci↗

arXiv2022

Tunable Ferromagnetism in LaCoO3 Epitaxial Thin Films

Ferromagnetic insulators play a crucial role in the development of low-dissipation quantum magnetic devices for spintronics. Epitaxial LaCoO3 thin film is a prominent ferromagnetic insulator, in which the robust ferromagnetic ordering emerges owing to epitaxial strain. Whereas it is evident that strong spin-lattice coupling induces ferromagnetism, the reported ferromagnetic properties of epitaxially strained LaCoO3 thin films were highly consistent. For example, even under largely modulated degree of strain, the reported Curie temperatures of epitaxially strained LaCoO3 thin films lie within 80-85 K, without much deviation. In this study, substantial enhancement (~18%) in the Curie temperature of epitaxial LaCoO3 thin films is demonstrated via crystallographic orientation dependence. By changing the crystallographic orientation of the films from (111) to (110), the crystal-field energy was reduced and the charge transfer between the Co and O orbitals was enhanced. These modifications led to a considerable enhancement of the ferromagnetic properties (including the Curie temperature and magnetization), despite the identical nominal degree of epitaxial strain. The findings of this study provide insights into facile tunability of ferromagnetic properties via structural symmetry control in LaCoO3.

cond-mat.mtrl-sci↗

arXiv2021

Defect engineering of magnetic ground state in EuTiO$_3$ epitaxial thin films

Atomistic defect engineering through the pulsed laser epitaxy of perovskite transition metal oxides offers facile control of their emergent opto-electromagnetic and energy properties. Among the various perovskite oxides, EuTiO3 exhibits a strong coupling between the lattice, electronic, and magnetic degrees of freedom. This coupling is highly susceptible to atomistic defects. In this study, we investigated the magnetic phase of EuTiO$_3$ epitaxial thin films via systematic defect engineering. A magnetic phase transition from an antiferromagnet to a ferromagnet was observed when the unit cell volume of EuTiO3 expanded due to the introduction of Eu-O vacancies. Optical spectroscopy and density functional theory calculations show that the change in the electronic structure as the ferromagnetic phase emerges can be attributed to the weakened Eu-Ti-Eu super-exchange interaction and the developed ferromagnetic Eu-O-Eu interaction. Facile defect engineering in EuTiO$_3$ thin films facilitates understanding and tailoring of their magnetic ground state.

cond-mat.mtrl-sci↗

arXiv2021

Data Analytics Approach to Predict High-Temperature Cyclic Oxidation Kinetics of NiCr-based Alloys

Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic-oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over a wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

cond-mat.mtrl-sci↗

arXiv2020

Coupling Physics in Machine Learning to Predict Properties of High-temperatures Alloys

High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9-12 wt.% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

cond-mat.mtrl-sci↗

arXiv2020

Solute-Vacancy Clustering in Aluminum

We present an extensive first-principles database of solute-vacancy, homoatomic, heteroatomic solute-solute, and solute-solute-vacancy binding energies of relevant alloying elements in aluminum. We particularly focus on the systems with major alloying elements in aluminum, i.e., Cu, Mg, and Si. We consider physical factors such as solute size and formation energies of intermetallic compounds to correlate with binding energies. Systematic studies of the homoatomic solute-solute-vacancy and heteroatomic (Cu, Mg, or Si)-solute-vacancy complexes reveal the overarching effect of the vacancy in stabilizing solute-solute pairs. The computed binding energies of the solute-solute-vacancy triplet successfully explain several experimental observations that remained unexplained by the reported pair binding energies in literature. The binding energy database presented here elucidates the interaction between solute cluster and vacancy in aluminum, and it is expected to provide insight into the design of advanced Al alloys with tailored properties.

cond-mat.mtrl-sci↗

arXiv2018

Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys

A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. The demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.

cond-mat.mtrl-sci↗

arXiv2013

Reversible redox reactions in an epitaxially stabilized SrCoOx oxygen sponge

Fast, reversible redox reactions in solids at low temperatures without thermomechanical degradation are a promising strategy for enhancing the overall performance and lifetime of many energy materials and devices. However, the robust nature of the cation's oxidation state and the high thermodynamic barrier have hindered the realization of fast catalysis and bulk diffusion at low temperatures. Here, we report a significant lowering of the redox temperature by epitaxial stabilization of strontium cobaltites (SrCoOx) grown directly as one of two distinct crystalline phases, either the perovskite SrCoO3-δ or the brownmillerite SrCoO2.5. Importantly, these two phases can be reversibly switched at a remarkably reduced temperature (200~300 °C) in a considerably short time (< 1 min) without destroying the parent framework. The fast, low temperature redox activity in SrCoO3-δ is attributed to a small Gibbs free energy difference between two topotatic phases. Our findings thus provide useful information for developing highly sensitive electrochemical sensors and low temperature cathode materials.

cond-mat.mtrl-sci↗

arXiv2007

First-principles study of ternary fcc solution phases from special quasirandom structures

In the present work, ternary Special Quasirandom Structures (SQSs) for a fcc solid solution phase are generated at different compositions, $x_A=x_B=x_C=\tfrac{1}{3}$ and $x_A=\tfrac{1}{2}$, $x_B=x_C=\tfrac{1}{4}$, whose correlation functions are satisfactorily close to those of a random fcc solution. The generated SQSs are used to calculate the mixing enthalpy of the fcc phase in the Ca-Sr-Yb system. It is observed that first-principles calculations of all the binary and ternary SQSs in the Ca-Sr-Yb system exhibit very small local relaxation. It is concluded that the fcc ternary SQSs can provide valuable information about the mixing behavior of the fcc ternary solid solution phase. The SQSs presented in this work can be widely used to study the behavior of ternary fcc solid solutions.

cond-mat.mtrl-sci↗

arXiv2007

Phase stability of hafnium oxide and zirconium oxide on silicon substrate

Phase stabilities of Hf-Si-O and Zr-Si-O have been studied with first-principles and thermodynamic modeling. From the obtained thermodynamic descriptions, phase diagrams pertinent to thin film processing were calculated. We found that the relative stability of the metal silicates with respect to their binary oxides plays a critical role in silicide formation. It was observed that both the HfO$_2$/Si and ZrO$_2$/Si interfaces are stable in a wide temperature range and silicide may form at low temperatures, partially at the HfO$_2$/Si interface.

cond-mat.mtrl-sci↗

arXiv2007

Thermodynamic modeling of the Hf-Si-O system

The Hf-O system has been modeled by combining existing experimental data and first-principles calculations results through the CALPHAD approach. Special quasirandom structures of $α$ and $β$ hafnium were generated to calculate the mixing behavior of oxygen and vacancies. For the total energy of oxygen, vibrational, rotational and translational degrees of freedom were considered. The Hf-O system was combined with previously modeled Hf-Si and Si-O systems, and the ternary compound in the Hf-Si-O system, HfSiO$_4$ has been introduced to calculate the stability diagrams pertinent to the thin film processing.

cond-mat.mtrl-sci↗

arXiv2007

Thermodynamic properties of binary HCP solution phases from special quasirandom structures

Three different special quasirandom structures (SQS) of the substitutional hcp $A_{1-x}B_x$ binary random solutions ($x=0.25$, 0.5, and 0.75) are presented. These structures are able to mimic the most important pair and multi-site correlation functions corresponding to perfectly random hcp solutions at those compositions. Due to the relatively small size of the generated structures, they can be used to calculate the properties of random hcp alloys via first-principles methods. The structures are relaxed in order to find their lowest energy configurations at each composition. In some cases, it was found that full relaxation resulted in complete loss of their parental symmetry as hcp so geometry optimizations in which no local relaxations are allowed were also performed. In general, the first-principles results for the seven binary systems (Cd-Mg, Mg-Zr, Al-Mg, Mo-Ru, Hf-Ti, Hf-Zr, and Ti-Zr) show good agreement with both formation enthalpy and lattice parameters measurements from experiments. It is concluded that the SQS's presented in this work can be widely used to study the behavior of random hcp solutions.

cond-mat.mtrl-sci↗