arXiv subjects

Peter K. Liaw

Publications and source records attributed to Peter K. Liaw.

At least 19 recordsLinked to original sources

arXiv2026

Enhanced Elevated-Temperature Strength in Refractory Complex Concentrated Alloys via Temperature-Induced Transition from Screw-to-Edge Dislocation Control

Refractory complex concentrated alloys (RCCAs) show promise for high-temperature applications but often lose strength due to screw-dislocation-controlled plasticity. We demonstrate a temperature-driven transition from screw- to edge-dislocation-controlled deformation in a single-phase NbTaTiV RCCA. Tensile tests from 298-1573 K reveal a pronounced intermediate-temperature strength plateau and yield strengths surpassing other ductile RCCAs and the Ni-based superalloy CMSX-4 above 1273 K. In-situ neutron diffraction, TEM, and molecular dynamics identify a crossover near ~900 K, where edge dislocation glide stabilized by V-induced lattice distortion dominates, enabling enhanced strength retention and a clear design strategy for ultrahigh-temperature applications.

cond-mat.mtrl-sci↗

arXiv2025

High-Throughput Exploration of Refractory High-Entropy Alloys for Strength and Plasticity

Refractory high-entropy alloys (RHEAs) are compositionally complex materials which have been demonstrated to have the potential for exceptional strength at high operating temperatures. However, their composition space is vast, and other property requirements, such as acceptable plasticity at room-temperature, must be met. Here, we leverage recently published, state-of-the-art deep learning models to predict compressive yield strength at 1,000 °C and room-temperature plasticity of >100,000 RHEAs. Multiple candidate materials were identified which exhibited exceptional balance between strength and plasticity. Upon experimental synthesis, multiple candidates were proven to outperform any previously reported RHEAs for simultaneous strength and plasticity. Our work demonstrates the power of data-driven approaches for rapid materials design, and enables continued multi-property optimization and materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Mechanistic Transition from Screw to Edge Dislocation Glide Enhances High-Temperature Strength in Refractory Complex Concentrated Alloys

The strength of body-centered cubic materials is traditionally known to be governed by screw dislocations. However, recent findings reveal that in certain refractory complex concentrated alloys, edge dislocations can instead control strength. This work integrates high-temperature mechanical testing, in-situ neutron scattering during heating and tension, scanning transmission electron microscopy, and molecular dynamics simulations to uncover the mechanism behind this behavior. In the Nb-Ta-Ti-V system, increasing the V content, due to its smaller atomic size, induces substantial atomic misfit that raises the glide barrier for edge dislocations relative to screw dislocations. This effect drives a gradual transition from screw to edge dislocation-controlled deformation, leading to markedly enhanced strength at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2025

From Quantum Annealing to Alloy Discovery: Towards Accelerated Design of High-Entropy Alloys

Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often get trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, that overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to handle large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2 % yield strength of 568 MPa, greater than 40 % compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery.

cond-mat.mtrl-sci↗

arXiv2024

High Strength Refractory AlHfNbTiV B2 High Entropy Alloys with High Fracture Strains

We demonstrate the development of a series of refractory high-entropy alloys containing aluminum AlRHEAs in the ordered BCC-B2 phase by varying the aluminum content within 10 to 25 atomic percent, with the goal of high strength and good ductility synergy. The AlRHEAs obtained are found to show promising potential for high-temperature applications. The incorporation of Al lowers the density and promotes the long-range atomic ordering, which in turn stabilizes the B2 formation, and strengthens the material but usually deteriorates ductility. Several B2 AlRHEAs that contain a combination of Ti, Hf, Nb, and V with moderate to high Poisson ratios are investigated for high strength and ductility. Furthermore, through statistical analysis, we identify a valley around the valence electron concentration VEC of 6 where low ductility is prominently observed. Machine-learning models are employed to screen the vast compositional space of AlRHEA alloys to predict B2 formation and toughness indicated by the yield strength and fracture strain. High prediction accuracies are achieved. As the Al content decreases, the B2 atomic ordering decreases, compression yield strengths decrease from 1500 MPa to 1200 MPa, and compression fracture strains increase from 0.06 to over 0.5. Notably, Al10Hf20Nb22Ti33V15 retains a compression yield strength exceeding 800 MPa up to 700 C, tensile yield strength of 1100 MPa, and fracture strain of 0.083. Our findings on enhancing ductility in pure B2 alloys pave the way for further research on Al-RHEA superalloys, striving to achieve high strength and ductility, reduced density, and improved oxidation resistance.

cond-mat.mtrl-sci↗

arXiv2023

Integrated Design of Aluminum-Containing High-entropy Refractory B2 Alloys with Synergy of High Strength and Ductility

Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations to effectively navigate phase-selection and mechanical-properties predictions, developing aluminum-containing RHEAs in single-phase ordered B2 alloys demonstrating both high strength and ductility. These aluminum-containing RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.6 GPa, fracture strains exceeding 50 percent, and significant high-temperature strength retention. They also demonstrate a tensile yield strength of 1.1 GPa with a tension ductility of 6.3 percent. Besides, we identify a valence-electron-count domain for alloy brittleness with the explanation from density-functional theory and provide crucial insights into elements' influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles that govern the mechanical properties of advanced structural alloys.

cond-mat.mtrl-sci↗

arXiv2023

Scaling and complexity of stress fluctuations associated with smooth and jerky flow in a FeCoNiTiAl high-entropy alloy

Recent observations of jerky flow in high-entropy alloys (HEA) revealed a high role of self-organization of dislocations in their plasticity. The present work reports first results of investigation of stress fluctuations during plastic deformation of a FeCoNiTiAl alloy, examined in a wide temperature range covering both smooth and jerky flow. These fluctuations, which accompany the overall deformation behavior representing an essentially slower stress evolution controlled by the work hardening, were processed using complementary approaches comprising the Fourier spectral analysis, the refined composite multiscale entropy, and multifractal formalisms. The joint analysis at distinct scales testified that even a macroscopically smooth plastic flow is accompanied with nonrandom fluctuations, disclosing self-organized dynamics of dislocations. Qualitative changes in such a fine-scale "noise" were found with varying temperature. The observed diversity is significant for understanding the relationships between different scales of plasticity of HEAs and crystal materials in general.

cond-mat.mtrl-sci↗

arXiv2023

An unprecedented synergy of high-temperature tensile strength and ductility in a NiCoCrAlTi high-entropy alloy

The present work reported a novel L12-strengthening NiCoCrAlTi high entropy alloy (HEA) with an outstanding synergy of tensile strength and ductility at both ambient and high temperatures. Transmission electron microscopy (TEM) characterization revealed a high density of rod-like and spheroidal L12 precipitates distributing in the micro/nanograins and non-recrystallized regions in the annealed specimens. The tremendously high yield stress, ultimate tensile stress (UTS), and ductility of the HEA at 600 C were ~1060 MPa, 1271 MPa, and 25%, respectively, which were significantly superior to most reported HEAs and Co- and Ni-based superalloys to date. Systematic TEM analysis unveiled that the cooperation among L12 precipitation, extensive stacking faults (SFs), deformation twins (DTs), immobile Lomer-Cottrell (L-C) locks formed from interactions between SFs and SFs/DTs, hierarchical SFs/DTs networks, as well as hetero-deformation-induced strengthening dominated the plastic deformation at 600 C. Such a unique deformation mechanism enabled extremely high tensile strength and sustained ductility of the HEA at a high temperature.

cond-mat.mtrl-sci↗

arXiv2023

Yield Strength-Plasticity Trade-Off and Uncertainty Quantification for Machine-learning-based Design of Refractory High-Entropy Alloys

Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a class of materials that are capable of excellent hightemperature properties. However, due to their multi-component nature, RHEAs have a vast composition space which presents challenges for traditional experimental exploration. Here, quantitative models of compressive yield strength and room-temperature plasticity are developed through a deep learning approach. Uncertainty quantification is performed through a variety of statistical validation techniques. Model predictions are experimentally validated through collection of recent literature and the synthesis and experimental characterization of two new, unreported RHEAs: AlMoTaTiZr and Al0.239Mo0.123Ta0.095Ti0.342Zr0.201. Finally, through the application of model interpretability, features having the greatest impact on both the mechanical property and uncertainty of the deep learning models are revealed, and shown to agree well with current physics and materials science theory.

cond-mat.mtrl-sci↗

arXiv2022

Physics-Based Machine Learning Approach for Modeling the Temperature-Dependent Yield Strength of Superalloys

In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a crucial role. In this paper a machine learning (ML) approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys, utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, $T_{break}$, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics, directly built into the model. We present a technique of global optimization, one allowing the concurrent optimization of model parameters over the low-temperature and high-temperature regimes. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.

physics.app-ph↗

arXiv2022

Physics-Based Machine-Learning Approach for Modeling the Temperature-Dependent Yield Strengths of Medium- or High-Entropy Alloys

Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use of reasonable machine-learning models. Here, we present a comprehensive and up-to-date overview of a bilinear log model for predicting temperature-dependent YS of medium-entropy or high-entropy alloys (MEAs or HEAs). In this model, a break temperature, Tbreak, is introduced, which can guide the design of MEAs or HEAs with attractive high-temperature properties. Unlike assuming black-box structures, our model is based on the underlying physics, incorporated in form of a priori information. A technique of global optimization is employed to enable the concurrent optimization of model parameters over low- and high-temperature regimes, showing that the break temperature is consistent across YS and ultimate strength for a variety of HEA compositions. A high-level comparison between YS of MEAs/HEAs and those of nickel-based superalloys reveal superior strength properties of selected refractory HEAs. For reliable operations, the temperature of a structural component, such as a turbine blade, made from refractory alloys may need to stay below Tbreak. Once above Tbreak, phase transformations may start taking place, and the alloy may begin losing structural integrity.

cond-mat.mtrl-sci↗

arXiv2021

Pseudoelastic deformation in Mo-based refractory multi-principal element alloys

Phase diagrams supported by density functional theory methods can be crucial for designing high-entropy alloys that are subset of multi-principal$-$element alloys. We present phase and property analysis of quinary (MoW)$_{x}$Zr$_{y}$(TaTi)$_{1-x-y}$ refractory high-entropy alloys from combined Calculation of Phase Diagram (CALPHAD) and density-functional theory results, supplemented by molecular dynamics simulations. Both CALPHAD and density-functional theory analysis of phase stability indicates a Mo-W-rich region of this quinary has a stable single-phase body-centered-cubic structure. We report first quinary composition from Mo$-$W$-$Ta$-$Ti$-$Zr family of alloy with pseudo-elastic behavior, i.e., hysteresis in stress$-$strain. Our analysis shows that only Mo$-$W$-$rich compositions of Mo$-$W$-$Ta$-$Ti$-$Zr, i.e., Mo$+$W$\ge$ 85 at.%, show reproducible hysteresis in stress-strain responsible for pseudo-elastic behavior. The (MoW)$_{85}$Zr$_{7.5}$(TaTi)$_{7.5}$ was down-selected based on temperature-dependent phase diagram analysis and molecular dynamics simulations predicted elastic behavior that reveals twinning assisted pseudoelastic behavior. While mostly unexplored in body-centered-cubic crystals, twinning is a fundamental deformation mechanism that competes against dislocation slip in crystalline solids. This alloy shows identical cyclic deformation characteristics during uniaxial $\lt$100$\gt$ loading, i.e., the pseudoelasticity is isotropic in loading direction. Additionally, a temperature increase from 77 to 1500 K enhances the elastic strain recovery in load-unload cycles, offering possibly control to tune the pseudoelastic behavior.

cond-mat.mtrl-sci↗

arXiv2021

The Serrated-Flow Behavior in High-Entropy Alloys

This chapter presents a literature review of the serrated flow phenomenon in high-entropy alloys (HEAs). The serrated flow is important as it can result in permanent macroscopic and microstructural changes in HEAs. The literature reveals several important findings regarding the effect of strain rate and test temperature on the serrated flow. Furthermore, this chapter explores the relationship among the composition, microstructure, testing condition, and serration behavior. Towards the end of the chapter, a concise summary is presented for the temperature, strain rate, mechanical-testing type (compression/tension/nanoindentation), and serration type for HEAs. This chapter also provides an overview of the different types of analytical methods that have been successfully implemented to model and analyze the serration behavior in HEAs. Such techniques include the mean-field theory (MFT) formalism, complexity-analysis method, and multifractal technique. Finally, future research topics in this area are presented, such as the effects of twinning and irradiation on the serration behavior.

cond-mat.mtrl-sci↗

arXiv2021

Charge-transfer effect on local lattice distortion in a HfNbTiZr high entropy alloy

It is often assumed that atoms are hard spheres in the estimation of local lattice distortion (LLD) in high-entropy alloys (HEAs). However, our study demonstrates that the hard sphere model misses the key effect, charge transfer among atoms with different electronegativities, in the understanding of the stabilization of severely-distorted HEAs. Through the characterization and simulations of the local structure of the HfNbTiZr HEA, we found that the charge transfer effect competes with LLD to significantly reduce the average atomic-size mismatch. Our finding may form the basis for the design of severely distorted, but stable HEAs.

cond-mat.mtrl-sci↗

arXiv2021

Mechanical behavior of high-entropy alloys: A review

High-entropy alloys (HEAs) are materials that consist of equimolar or near-equimolar multiple principal components but tend to form single phases, which is a new research topic in the field of metallurgy, have attracted extensive attention in the past decade. The HEAs families contain the face-centered-cubic (fcc), body-centered-cubic (bcc), and hexagonal-close-packed (hcp)-structured HEAs. On one hand, mechanical properties, e.g. hardness, strength, ductility, fatigue, and elastic moduli, are essential for practical applications of HEAs. Scientists have explored in this direction since the advent of HEAs. On the other hand, the pursuit of high strength and good plasticity is the critical research issue of materials. Hence, strengthening of HEAs is a crucial issue. Recently, many articles are focusing on the strengthening strategies of HEAs[1-14]. In this chapter, we reviewed the recent work on the room-temperature elastic properties and mechanical behavior of HEAs, including the mechanisms behind the plastic deformation of HEAs at both low and high temperatures. Furthermore, the present work examined the strengthening strategies of HEAs, e.g. strain hardening, grain-boundary strengthening, solid-solution strengthening, and particle strengthening. The fatigue, creep, and fracture properties were briefly introduced. Lastly, the future scientific issues and challenges of HEAs were discussed.

cond-mat.mtrl-sci↗

arXiv2021

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

cond-mat.mtrl-sci↗

arXiv2020

Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the corresponding driving (input) factors are identified. These input factors may include the material composition, heat treatment, manufacturing process, microstructure, temperature, strain rate, environment, or testing mode. The framework assumes the selection of an optimization technique suitable for the application at hand and the data available. Physics-based models are presented, such as for predicting the ultimate tensile strength (UTS) or fatigue resistance. We devise models capable of accounting for physics-based dependencies. We factor such dependencies into the models as a priori information. In case that an artificial neural network (ANN) is deemed suitable for the applications at hand, it is suggested to employ custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and for extracting the most out of the - usually limited - input data available.

cond-mat.mtrl-sci↗

arXiv2018

On the Study of Chaos and Memory Effects in the Bonhoeffer-van der Pol Oscillator with a Non-Ideal Capacitor

In this paper, the voltage fluctuations of the Bonhoeffer van der pol oscillator system with a non-ideal capacitor were investigated. Here, the capacitor was modeled, using a fractional differential equation in which the order of the fractional derivative is also a measure of the memory in the dielectric. The governing fractional differential equation was derived using two methods, namely a differential and integral approach. The former method utilized a hierarchical resistor-capacitor (RC) ladder model while the latter utilized the theory of the universal dielectric-response. The dynamical behavior of the potential across the capacitor was found to be affected by this parameter, and, therefore, the memory of the system. Additionally, findings indicate that an increase in the memory parameter was associated with an increase in the energy stored in the dielectric. It was found that oscillation death resulted in a higher amount of stored energy in the dielectric over time, as compared to behavior, which displayed relaxation oscillations or chaotic fluctuations. The relatively-lower stored energy resulting from the latter types of dynamical behavior appeared to be a consequence of the memory effect, where present accumulations of energy in the capacitor are affected by previous decreases in the potential. Hence, in this type of scenario, the dielectric material can be thought of as remembering the past behavior of the voltage, which leads to either a decrease, or an enhancement in the stored energy. The non-ideal capacitor was also found to have a transitory nature, where it behaves more like a resistor as α approaches 0, and conversely, more like a capacitor as α goes to 1. Here, a decrease in α was linked to an enhanced metallic character of the dielectric.

physics.data-an↗