arXiv subjects

Shuang Lin

Publications and source records attributed to Shuang Lin.

7 recordsLinked to original sources

arXiv2026

Phase stability and mechanical response of Ag-interlayered Al/Cu resistance spot-welded joints

Dissimilar Al/Cu joints are essential to battery-pack assemblies; however, their mechanical strength is limited by brittle Al-Cu intermetallic compounds (IMCs) such as Al2Cu and Al4Cu9. Interlayer strategies to suppress these phases remain largely empirical, lacking a predictive framework linking interlayer chemistry to the phases that form and to their intrinsic mechanical character. Here an Ag interlayer is introduced and combines computational thermodynamics, first-principles calculations, microstructural characterization, and mechanical testing into a single self-consistent description of the joint. CALculation of PHAse Diagrams (CALPHAD) equilibrium and Scheil simulations predict the solidification path of the Al-rich Al-Ag fusion zone and explain why Cu incorporation is limited when Ag is present; energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) confirm an FCC Al-Ag solid solution as the dominant constituent. First-principles phonon calculations within the quasiharmonic approximation yield finite-temperature entropy and Gibbs energy, benchmarked against CALPHAD, while elastic constants assess ductility via the Pugh criterion (i.e., the bulk/shear (B/G) modulus ratio). All Al-Ag phases, including the observed solid solution, exceed the Pugh threshold of 1.75, whereas the targeted Al-Cu IMCs do not, giving a mechanistic basis for the interlayer's effectiveness. This microstructural change translates into improved performance: nominal strength rises from 47.9 to 67.4 MPa. Nanoindentation gives a fusion-zone reduced modulus of 82.8 GPa (Young's modulus 82.0 GPa), versus a calculated 0 K Voigt-Reuss-Hill value of 71.4 GPa. The present work establishes a transferable CALPHAD, first-principles, and experiment workflow for rational interlayer selection in dissimilar-metal joining.

cond-mat.mtrl-sci↗

arXiv2025

Thermodynamic modeling of binaries in Cr-Fe-Mo-Nb-Ni supported by first-principles calculations

Thermodynamic descriptions of all binaries within the Cr-Fe-Mo-Nb-Ni system have been complied and, where necessary, remodeled. Notably, the Cr-Fe and Fe-Mo systems have been remodeled using comprehensive sublattice models for the topologically close-packed (TCP) phases of Laves_C14, sigma, and mu according to their Wyckoff positions. These refinements are supported by first-principles calculations based on density functional theory (DFT), in conjunction with available experimental data in the literature. The resulting models offer improved accuracy in describing the TCP phases. For instance, the predicted site occupancies of sigma in Cr-Fe show excellent agreement with experimental observations. The present work provides a robust foundation for CALPHAD modeling and the design of complex, multi-component materials, particularly those based on Fe-based and Ni-based alloys.

cond-mat.mtrl-sci↗

arXiv2024

Investigation of ideal shear strength of dilute binary and ternary Ni-based alloys using first-principles calculations, CALPHAD modeling and correlation analysis

In the present work, the ideal shear strength ({\tau}_is) of dilute Ni34XZ ternary alloys (X or Z = Al, Co, Cr, Fe, Mn, Mo, Nb, Si, Ti) are predicted by first-principles calculations based on density functional theory (DFT) in terms of pure alias shear deformations. The {\tau}_is results show that within the concentration up to 8.3% of alloying elements, {\tau}_is increases with composition in binary systems with Mn, Fe, and Co in ascending order, and decreases with composition with Nb, Si, Mo, Ti, Al, and Cr in descending order. The composition dependence of {\tau}_is in binary and ternary systems is modeled using the CALculation of PHAse Diagrams (CALPHAD) approach considering lattice instability, indicating that atomic bonding strength significantly influences {\tau}_is. Correlational analyses further show that lattice constant and elastic constant C11 affect {\tau}_is, the most out of the elemental features.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-dependent thermodynamic properties of CrNbO4 and CrTaO4 by first-principles calculations

In the present work, the density functional theory (DFT) in the generalized-gradient approximation developed by Perdew, Burke, and Ernzerhof (PBE) +U method, i.e., PBE+U, was employed to predict temperature-dependent thermodynamic properties of the rutile-type oxides CrNbO4 and CrTaO4 as well as the binary oxides Cr2O3, Nb2O5, and Ta2O5 via the quasiharmonic phonon approach (QHA). Calculated thermodynamic properties of the binary oxides were benchmarked with experimental data, showing high accuracy except for the negative thermal expansion (NTE) of Nb2O5, attributed to its polymorphic complexity. By combining the formation energy predicted by DFT with the existing SGTE Substances Database (SSUB5), the CrNbO4 and CrTaO4 are found to be thermodynamic stable up to 1706 K and 1926 K and decompose into Cr2O3 and Nb2O5 or Ta2O5 at those temperatures, respectively. The temperature dependence of linear thermal expansion coefficients for CrNbO4 and CrTaO4 are predicted, and their mean values from 500 K to 2000 K are found to be 6.0*10-6/K and 5.04*10-6/K, respectively, in agreement with experimental observations in the literature. The gas-phase species and their vapor pressure are calculated, indicating that the formation of CrTaO4 and CrNbO4 reduces chromium volatilization, which is critically important to design enhanced Refractory high entropy alloys (RHEAs) with enhanced oxidation resistance.

cond-mat.mtrl-sci↗

arXiv2023

Predictions and correlation analyses of Ellingham diagrams in binary oxides

Knowing oxide-forming ability is vital to gain desired or avoid deleterious oxides formation through tuning oxidizing environment and materials chemistry. Here, we have conducted a comprehensive thermodynamic analysis of 137 binary oxides using the presently predicted Ellingham diagrams. It is found that the active elements to form oxides easily are the f-block elements (lanthanides and actinides), elements in the groups II, III, and IV (alkaline earth, Sc, Y, Ti, Zr, and Hf), and Al and Li; while the noble elements with their oxides nonstable and easily reduced are coinage metals (Cu, Ag, and especially Au), Pt-group elements, and Hg and Se. Machine learning based sequential feature selection indicates that oxide-forming ability can be represented by electronic structures of pure elements, for example, their d- and s-valence electrons, Mendeleev numbers, and the groups, making the periodic table a useful tool to tailor oxide-forming ability. The other key elemental features to correlate oxide-forming ability are thermochemical properties such as melting points and standard entropy at 298 K of pure elements. It further shows that the present Ellingham diagrams enable qualitatively understanding and even predicting oxides formed in multicomponent materials, such as the Fe-20Cr-20Ni alloy (in wt.%) and the equimolar high entropy alloy of AlCoCrFeNi, which are in accordance with thermodynamic calculations using the CALPHAD approach and experimental observations in the literature.

cond-mat.mtrl-sci↗

arXiv2023

Comparing Forward and Inverse Design Paradigms: A Case Study on Refractory High-Entropy Alloys

The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates to determine the best candidate that matches the target properties. However, recent advances in the field of deep learning have given rise to the possibility of an ``inverse'' design paradigm for advanced materials, wherein a model provided with the target properties is able to find the best candidate. Being a relatively new concept, there remains a need to systematically evaluate how these two paradigms perform in practical applications. Therefore, the objective of this study is to directly, quantitatively compare the forward and inverse design modeling paradigms. We do so by considering two case studies of refractory high-entropy alloy design with different objectives and constraints and comparing the inverse design method to other forward schemes like localized forward search, high throughput screening, and multi objective optimization.

cond-mat.mtrl-sci↗

arXiv2021

Generative deep learning as a tool for inverse design of high-entropy refractory alloys

Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics.

cond-mat.mtrl-sci↗