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Shin, Dongwon

Publications and source records attributed to Shin, Dongwon.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The many faces of θ' -Al 2 Cu precipitates: Energetics of pristine and solute segregated Al/ θ' semi-coherent interfaces

θ'-Al 2 Cu precipitates in Al-Cu alloys have various distorted octagon shapes, which can be explained by the competition between {100} and {110} type semi-coherent interfaces with the Al matrix. While most prior studies on the semi-coherent Al/θ' interfaces have focused on the {100} orientation, little is known about the {110} interface. We have investigated the energetics of pristine and solute-segregated {110} semi-coherent Al/θ' interfaces with advanced characterization and first-principles studies. We report interfacial, strain, and solute segregation energetics of the {110} Al/θ' semi-coherent interface for 39 elements and compared them with previously reported values of the {100} interface. We discuss the atomic features and atomic local structures to identify similarities and differences between the two types of Al/θ' semi-coherent interfaces. Here, the isotropy in pristine Al/θ' semi-coherent interfacial energy and the anisotropy resulting from solute segregation provide insight into the formation of different types of θ' precipitate “faces” reported in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of microalloying additions on microstructural evolution and thermal stability in cast Al-Ni alloys

Enhancement of thermal stability in Al-Ni alloys through microalloying with slow-diffusing elements, specifically Zr, has been previously reported which is attributed to Zr segregation at the Al/Al 3 Ni interface. In this study, we explore the influence of microalloying Al-Ni alloys with Zr, Ti, V, and Fe on microstructural evolution, hardness, and electrical and thermal conductivity across a range of heat-treatment temperatures from 300 to 450 °C. The distribution of microalloying elements and precipitates after heat treatment is characterized using atom probe tomography (APT). Our investigation confirms Zr segregation to the Al/Al 3 Ni interface, while similar interfacial segregation is absent with the addition of Ti, V, and Fe. Additionally, our analysis of the Al 3 Ni microfiber morphology reveals that their coarsening and spheroidization rates are similar with and without interfacial segregation; thus, retaining the fiber reinforcement through interfacial segregation of slow diffusing elements may not be an effective strategy. Precipitation of L1 2 nanoparticles was found to be the dominant mechanism affecting enhanced hardness and electrical conductivity in Al-Ni-Zr alloys, attributed to precipitation strengthening and solute depletion, respectively. Similar precipitation was not observed for additions of Ti, V, and Fe following heat treatment. We provide a thermodynamic explanation for this limitation. Furthermore, the findings of this study suggest that an effective approach for designing Al-Ni alloys should involve prioritizing microalloying elements to maximize L1 2 precipitation and minimize solute content in the FCC-Al matrix post heat treatment, rather than focusing on Al/Al3Ni interfacial segregation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Atomistic Probing of Defect-Engineered 2H-MoTe 2 Monolayers

Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe 2 monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe 2 samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe 2 , respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe 2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality.

2H-MoTe 2↗
DOE OSTI2023

Effects of Sn addition on precipitation of Al–Zn–Mg alloy at early stage of natural aging

The effect of Sn addition on the natural aging behavior of Al–Zn–Mg alloy was investigated using atom probe tomography (APT) experiments and density functional theory (DFT) calculations. The results demonstrate that Sn retards the natural aging behavior by facilitating the preferential formation of Mg–Sn clusters, which suppress the formation of Guinier-Preston zones (I) during the earliest aging stage. Quantitative solute analysis conducted by APT has suggested increasing amount of Mg–Sn clusters in Sn-contained alloy due to their high binding energy, supported by DFT calculations on the Sn.

36 MATERIALS SCIENCE↗
DOE OSTI2022

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

Here, 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.

42 ENGINEERING↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

First-principles study of Al/Al 3 Ni interfaces

Al-Ni alloys have shown promise for high-temperature applications due to the strengthening of Al 3 Ni fibers resistant to coarsening and spheroidization up to 400°C. While the interface between Al and Al 3 Ni phases affects the coarsening rate of Al 3 Ni at elevated temperatures, its characteristics are largely unknown to date. Here, we have constructed various supercells to model this interface and performed a first-principles study based on density functional theory (DFT). We have considered three groups of Al/Al 3 Ni interfaces: experimentally reported orientation relationships from the solidification studies, crystallographically similar Fe-Fe 3 C pearlite interfaces, and the family of low-index (100) termination planes. We have analyzed the correlation between the DFT Al/Al 3 Ni interfacial energies and characteristic features, e.g., excess free volume and the number of broken bonds. We outline the further experimental and computational analysis required to improve the interface modeling of Al/Al 3 Ni.

36 MATERIALS SCIENCE↗
DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

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 relations. Herein, we employ an emerging data analytics approach to quantitatively evaluate the underlying relationships between critical thickness ($h_c$) of strain relaxation and various physical and chemical features, despite the sporadic experimental data points available. First, we have collected and refined the reported $h_c$ of the 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 includes 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. In this work, the demonstrated framework is expected to be efficiently extended to the other classes of thin films in understanding $h_c$.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase stability in cast and additively manufactured Al-rich Al-Cu-Ce alloys

Additively manufactured (AM) eutectic Al alloy systems have been studied extensively for advantageous thermal stability and mechanical properties due to their refined microstructures. Al-Cu-Ce alloys are one subset of these AM eutectic alloys. Here we studied phase stability in AM Al-Cu-Ce alloys and compared it to that of conventionally cast ones. A new phase, Al 8 Cu 3 Ce, was identified in the microstructures of both AM and cast Al-Cu-Ce alloys. This Al 8 Cu 3 Ce phase was not previously included on experimental or thermodynamically calculated phase diagrams of the Al-Cu-Ce system. Therefore, we performed additional thermodynamic modeling of the system. These models were experimentally validated with cast and subsequently heat-treated Al-Cu-Ce alloys. We found that despite the refined microstructure of the AM alloys, the phases formed were consistent with the cast alloys, suggesting that AM processing did not significantly alter the formation and stability of phases from that in the conventional alloys. In conclusion, this work has resolved inconsistent previous descriptions of the Al-Cu-Ce ternary phase diagram in the Al-rich region and resulted in the addition of the Al 8 Cu 3 Ce as an equilibrium phase above 500 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tunable Ferromagnetism in LaCoO 3 Epitaxial Thin Films

We report ferromagnetic insulators play a crucial role in the development of low-dissipation quantum magnetic devices for spintronics. Epitaxial LaCoO 3 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 LaCoO 3 thin films are highly consistent. For example, even under largely modulated degree of strain, the reported Curie temperatures of epitaxially strained LaCoO 3 thin films lie in a narrow range of 80–85 K, without much deviation. In this study, substantial enhancement (≈18%) in the Curie temperature of epitaxial LaCoO 3 thin films is demonstrated via crystallographic orientation dependence. By changing the crystallographic orientation of the films from (111) to (110), the crystal-field energy is reduced and the charge transfer between the Co and O orbitals is enhanced. These modifications lead 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 LaCoO 3 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Aluminum alloy compositions and methods of making and using the same

The present disclosure concerns embodiments of aluminum alloy compositions exhibiting superior microstructural stability and strength at high temperatures. The disclosed aluminum alloy compositions comprise particular combinations of components that contribute the ability of the alloys to exhibit improved microstructural stability and hot tearing resistance as compared to conventional alloys. Also disclosed herein are embodiments of methods of making and using the alloys.

Shyam, Amit↗
DOE OSTI2022

Aluminum alloy compositions and methods of making and using the same

The present disclosure concerns embodiments of aluminum alloy compositions exhibiting microstructural stability and strength at high temperatures. The disclosed aluminum alloy compositions comprise particular combinations of components that contribute the ability of the compositions to exhibit improved microstructural stability and hot tearing resistance as compared to conventional alloys. Also disclosed herein are embodiments of methods of making and using the alloys.

Shyam, Amit↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Atomic structures of interfacial solute gateways to θ' precipitates in Al-Cu alloys

Many materials employed in critical structural applications depend upon metastable strengthening precipitates that transform or dissolve at elevated temperatures. Herein, aberration-corrected scanning transmission electron microscopy and first-principles calculations are used to accurately determine the atomic structure of the highly mobile, semi-coherent precipitate interfaces that control this process in the classic θ' (Al 2 Cu) precipitate in the Al-Cu system. Semi-coherent {110} interfaces are found to be composed of an array of unexpected misfit dislocations that are arranged in two different structural units. Dislocations accommodate nearly all of the misfit between the Al matrix and strengthening phase. Cu is observed to segregate to the compressed edge of the dislocation cores at specific sites in this interface. First-principles calculations revealed the energetic landscape that facilitates these sites to become entry and exit gateways of Cu atoms in this semi-coherent interface. In conclusion, this investigation reveals critical features within semi-coherent interfaces that determine the thermal stability of precipitation-hardened alloys.

36 MATERIALS SCIENCE↗