arXiv subjects

Penghui Cao

Publications and source records attributed to Penghui Cao.

At least 19 recordsLinked to original sources

arXiv2026

Fragmenting Diffusion Pathways Confers Extraordinary Radiation Resistance in Refractory Multicomponent Alloys

The accumulation and growth of vacancy clusters under irradiation is a pivotal degradation mode for structural materials in extreme environments. Even tungsten undergoes rapid defect coarsening compromising its integrity. Here we show a tungsten multicomponent alloy that effectively fragments the vacancy diffusion network, kinetically trapping defects within localized domains. This effect originates from a broad spectrum of migration barriers and substantial vacancy-jump heterogeneity, which drives the interconnectivity of diffusion paths below the percolation threshold. Starving clusters of the necessary vacancy supply, irradiation experiments and atomic-scale defect characterizations confirm negligible defect growth as radiation doses increase by four orders of magnitude. These results provide a fundamental paradigm for percolation-engineered kinetics, offering a predictive pathway for tailoring defect diffusion and discovering inherently radiation-tolerant materials.

cond-mat.mtrl-sci↗

arXiv2025

Local chemical order suppresses grain boundary migration under irradiation in CrCoNi

Complex concentrated alloys with intrinsic chemical heterogeneity are promising candidates for nuclear applications, where local chemical order can strongly influence defect evolution under irradiation. Grain boundaries also contribute to radiation damage mitigation by serving as defect sinks, yet this interaction can alter interfacial structure, typically leading to destabilization and grain growth. This study investigates how chemical ordering influences grain boundary migration and stability during successive radiation events in CrCoNi. Using atomistic simulations, bicrystals were equilibrated to induce segregation-enhanced chemical order, followed by prolonged irradiation at 1100 K. Our results show that grain boundaries in random CrCoNi begin to migrate after only a few collision cascades, whereas those in the ordered alloy remain immobile until the chemical order is sufficiently disrupted. Single-cascade simulations reveal key mechanistic differences, where cascades near chemically ordered interfaces produce smaller damage volumes and reduced atomic displacement due to enhanced Frenkel pair combination within the cascade core. This limits both the residual defect population and the energetic driving force for interfacial rearrangement. Subsequent simulations of irradiated interfaces show that interstitial absorption induces a structural transition that modifies the segregation morphology at and near the grain boundary, demonstrating a dynamic coupling between ordering stability and defect evolution. These findings offer new insights into the role of local chemical order on defect-interface interactions under extreme conditions and highlight pathways for designing radiation-tolerant materials for next-generation nuclear systems.

cond-mat.mtrl-sci↗

arXiv2025

Phase transitions of eutectic high entropy alloy AlCoCrFeNi2.1 under shock compression

High entropy alloys (HEAs) are a new class of metals that exhibit unique mechanical performance. Among HEAs, additively manufactured eutectic high entropy alloys (AM-EHEAs) have recently emerged as candidate materials for use in extreme conditions due to their simultaneous high strength and ductility. However, the deformation and structural evolution of AM-EHEAs under conditions of high pressure have not been well characterized, limiting their use in extreme applications. We present dynamic compression experiments and molecular dynamics simulations studying the structural evolution of AM-EHEA AlCoCrFeNi2.1 when compressed to pressures up to 400 GPa. Our in-situ X-ray diffraction measurements capture the appearance of fcc and bcc phases at different pressure conditions, with pure- and mixed-phase regions. Understanding the phase stability and structural evolution of the AM EHEA offers new insights to guide the development of high-performance complex materials for extreme conditions.

cond-mat.mtrl-sci↗

arXiv2025

Grain boundaries amplify local chemical ordering in complex concentrated alloys

Local chemical ordering strongly influences the behavior of complex concentrated alloys, yet its characterization remains challenging due to the nanoscale dimensions and scattered spatial distribution of the ordered domains. Here, we study chemical ordering near grain boundaries, demonstrating they can act as microstructural anchor points that amplify chemical order and drive the formation of compositional nanopatterns. Atomistic simulations reveal the development of composition waves with ordering vectors normal to the boundary plane in two distinct material systems, CrCoNi and NbMoTaW. These waves manifest as periodic enrichment-depletion patterns that reflect the underlying chemical ordering tendencies of each system, but with amplified contrast that extends several nanometers into the grain interior before gradually decaying. By examining multiple grain boundary orientations and alloys, we show that both the interfacial segregation profile and the crystallographic terminating plane govern the extent and character of this amplification. This interplay between boundary-dictated directional ordering and the diffuse, untemplated chemical domain evolution within the grain advances our understanding of interface-mediated ordering phenomena and suggests new opportunities for experimentally detecting local chemical order in complex concentrated alloys.

cond-mat.mtrl-sci↗

arXiv2024

Divergent Evolution of Slip Banding in Alloys

Metallic materials under high stress often exhibit deformation localization, manifesting as slip banding. Over seven decades ago, Frank and Read introduced the well-known model of dislocation multiplication at a source, explaining slip band formation. Here, we reveal two distinct types of slip bands (confined and extended) in alloys through multi-scale testing and modeling from microscopic to atomic scales. The confined slip band, characterized by a thin glide zone, arises from the conventional process of repetitive full dislocation emissions at Frank-Read source. Contrary to the classical model, the extended band stems from slip-induced deactivation of dislocation sources, followed by consequent generation of new sources on adjacent planes, leading to rapid band thickening. Our findings provide critical insights into atomic-scale collective dislocation motion and microscopic deformation instability in advanced structural materials, marking a pivotal advancement in our fundamental understanding of deformation dynamics.

cond-mat.mtrl-sci↗

arXiv2024

Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments

Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchical machine learning (ML) to unveil the complex relationships between a large set of process parameters in Laser Powder Bed Fusion (LPBF) and selected mechanical properties (tensile strength and ductility). The HT method envisions the fabrication of small samples for rapid automated hardness and porosity characterization, and a smaller set of tensile specimens for more labor-intensive direct measurement of yield strength and ductility. The ML approach is based on a sequential application of Gaussian processes (GPs) where the correlations between process parameters and hardness/porosity are first learnt and subsequently adopted by the GPs that relate strength and ductility to process parameters. Finally, an optimization scheme is devised that leverages these GPs to identify the processing parameters that maximize combinations of strength and ductility. By founding the learning on larger easy-to-collect and smaller labor-intensive data, we reduce the reliance on expensive characterization and enable exploration of a large processing space. Our approach is material-agnostic and herein we demonstrate its application on 17-4PH stainless steel.

cs.LG↗

arXiv2024

A simple model for short-range ordering kinetics in multi-principal element alloys

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. The advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature make it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

cond-mat.mtrl-sci↗

arXiv2024

A Machine Learning Framework for the Prediction of Grain Boundary Segregation in Chemically Complex Environments

The discovery of complex concentrated alloys has unveiled materials with diverse atomic environments, prompting the exploration of solute segregation beyond dilute alloys. Data-driven methods offer promising for modeling segregation in such chemically complex environments, and are employed in this study to understand segregation behavior of a refractory complex concentrated alloy, NbMoTaW. A flexible methodology is developed that uses composable computational modules, with different arrangements of these modules employed to obtain site availabilities at absolute zero and the corresponding density of states beyond the dilute limit, resulting in an extremely large dataset containing 10 million data points. The artificial neural network developed here can rely solely on descriptions of local atomic environments to predict behavior at the dilute limit with very small errors, while the addition of negative segregation instance classification allows any solute concentration from zero up to the equiatomic concentration for ternary or quaternary alloys to be modeled at room temperature. The machine learning model thus achieves a significant speed advantage over traditional atomistic simulations, being four orders of magnitude faster, while only experiencing a minimal reduction in accuracy. This efficiency presents a powerful tool for rapid microstructural and interfacial design in unseen domains. Scientifically, our approach reveals a transition in the segregation behavior of Mo from unfavorable in simple systems to favorable in complex environments. Additionally, increasing solute concentration was observed to cause anti-segregation sites to begin to fill, challenging conventional understanding and highlighting the complexity of segregation dynamics in chemically complex environments.

cond-mat.mtrl-sci↗

arXiv2024

Atomic-scale tunable phonon transport at tailored grain boundaries and Their Impact on Thermal Conductivity

Grain boundaries (GBs) strongly influence thermal transport in crystalline solids by disrupting lattice periodicity and scattering phonons. Due to the atomic-level disorder and structural complexity, a fundamental understanding of how specific GB geometries regulate nanoscale phonon behavior and macroscopic thermal conductivity has remained elusive. Here, using emerging atomic-resolution vibrational spectroscopy, we directly correlate GB structure, defect-specific vibrational states, and thermal transport in bicrystal strontium titanate with controlled tilt and twist angles. The phonon characterizations and thermal conductivity data reveal two distinct regimes, where low-angle tilt GBs (2deg, 6deg, 10deg) substantially modulate phonon populations and mode frequencies, resulting in pronounced changes in thermal conductivity, whereas high-angle tilt GBs (22deg, 36deg) exhibit weak conductivity variation due to saturated structural disorder and scattering. In contrast, twist GBs introduce periodic defect motifs that only locally tailor phonon transport. Our results suggest tilt and twist angles as complementary knobs for coarse and fine control of phonon propagation and cross-GB thermal transport, providing a predictive framework for thermal engineering in materials.

cond-mat.mes-hall↗

arXiv2024

Neural Network Kinetics for Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. Local chemical complexity in compositionally complex alloys poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. Using this method, we study the temperature-dependent local chemical ordering in a refractory Nb-Mo-Ta alloy and reveal a critical temperature at which the B2 order reaches a maximum. Our atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

cond-mat.dis-nn↗

arXiv2024

Frustrated metastable-to-equilibrium grain boundary structural transition in NbMoTaW due to segregation and chemical complexity

Grain boundary structural transitions can lead to significant changes in the properties and performance of materials. In multi-principal element alloys, understanding these transitions becomes complex due to phenomena such as local chemical ordering and multi-component segregation. Using atomistic simulations, we explore a metastable-to-equilibrium grain boundary structural transition in NbMoTaW. The transition, characterized by structural disordering and reduced free volume, shows high sensitivity to its local chemical environment. Most notably, the transition temperature range of the alloy is more than twice that of a pure metal. Differences in composition between coexisting metastable and equilibrium structures highlight the change in local site availability due to structural relaxation. Further examination of grain boundaries with fixed chemical states at varying temperatures reveals that the amount of segregation significantly influences the onset temperature yet has minimal effect on the transition width. These insights underscore the profound effects of chemical complexity and ordering on grain boundary transitions in complex concentrated alloys, marking a meaningful advancement in our understanding of grain boundary behavior at the atomic level.

cond-mat.mtrl-sci↗

arXiv2024

Ubiquitous short-range order in multi-principal element alloys

Recent research in multi-principal element alloys (MPEAs) has increasingly focused on the exploration and exploitation of short-range order (SRO) to enhance material performance. However, the understanding of SRO formation and the precise tuning of it within MPEAs remains poorly understood, limiting the comprehension of its impact on material properties and impeding the advancement of SRO engineering. Here, leveraging advanced additive manufacturing techniques that produce samples with a wide range of cooling rates (up to 10^7 K/s) and an improved quantitative electron microscopy method, we characterize SRO in three CoCrNi-based MPEAs to unravel the role of processing route and thermal history on SRO. Surprisingly, irrespective of the processing and thermal treatment applied, all samples exhibit similar levels of SRO, suggesting that prevalent SRO may form during the solidification process. Atomistic simulations of solidification verify that local chemical ordering arises in the liquid-solid interface (solidification front) even under the extreme cooling rate of 10^11 K/s. This phenomenon stems from the swift atomic diffusion in the supercooled liquid, which matches or even surpasses the rate of solidification. Therefore, SRO is an inherent characteristic of most MPEAs, insensitive to variations in cooling rates and annealing treatments typically available in experiments. Integrating thermal treatment with other strategies, such as mechanical deformation and irradiation, might be more effective approaches for harnessing SRO to achieve controlled material properties.

cond-mat.mtrl-sci↗

arXiv2023

Enhanced radiation damage tolerance of amorphous interphase and grain boundary complexions in Cu-Ta

Amorphous interfacial complexions are particularly resistant to radiation damage and have been primarily studied in alloys with good glass-forming ability, yet recent reports suggest that these features can form even in immiscible alloys such as Cu-Ta under irradiation. In this study, the mechanisms of damage production and annihilation due to primary knock-on atom collisions are investigated for amorphous interphase and grain boundaries in a Cu-Ta alloy using atomistic simulations. Amorphous complexions, in particular amorphous interphase complexions that separate Cu and Ta grains, result in less residual defect damage than their ordered counterparts. Stemming from the nanophase chemical separation in this alloy, the amorphous complexions exhibit a highly heterogeneous distribution of atomic excess volume, as compared to a good glass former like Cu-Zr. Complexion thickness, a tunable structural descriptor, plays a vital role in damage resistance. Thicker interfacial films are more damage-tolerant because they alter the defect production rate due to differences in intrinsic displacement threshold energies during the collision cascade. Overall, the findings of this work highlight the importance of interfacial engineering in enhancing the properties of materials operating in radiation-prone environments and the promise of amorphous complexions as particularly radiation damage-tolerant microstructural features.

cond-mat.mtrl-sci↗

arXiv2023

Exceptional electronic transport and quantum oscillations in thin bismuth crystals grown inside van der Waals materials

Confining materials to two-dimensional forms changes the behavior of electrons and enables new devices. However, most materials are challenging to produce as uniform thin crystals. Here, we present a new synthesis approach where crystals are grown in a nanoscale mold defined by atomically-flat van der Waals (vdW) materials. By heating and compressing bismuth in a vdW mold made of hexagonal boron nitride (hBN), we grow ultraflat bismuth crystals less than 10 nanometers thick. Due to quantum confinement, the bismuth bulk states are gapped, isolating intrinsic Rashba surface states for transport studies. The vdW-molded bismuth shows exceptional electronic transport, enabling the observation of Shubnikov-de Haas quantum oscillations originating from the (111) surface state Landau levels, which have eluded previous studies. By measuring the gate-dependent magnetoresistance, we observe multi-carrier quantum oscillations and Landau level splitting, with features originating from both the top and bottom surfaces. Our vdW-mold growth technique establishes a platform for electronic studies and control of bismuth's Rashba surface states and topological boundary modes. Beyond bismuth, the vdW-molding approach provides a low-cost way to synthesize ultrathin crystals and directly integrate them into a vdW heterostructure.

cond-mat.mes-hall↗

arXiv2022

Influence of chemistry and structure on interfacial segregation in NbMoTaW with high-throughput atomistic simulations

Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial segregation and the underlying structural and chemical driving factors. Sampling hundreds of bicrystals along the [001] symmetric tilt axis and analyzing more than one hundred and thirty thousand grain boundary sites with a variety of local atomic environments, we uncover segregation trends in NbMoTaW. While Nb is the dominant segregant, more notable are the segregation patterns that deviate from expected behavior and mark situations where local structural and chemical driving forces lead to interesting segregation events. For example, incomplete depletion of Ta in low-angle boundaries results from chemical pinning due to favorable local compositional environments associated with chemical short-range ordering. Finally, machine learning models capturing and comparing the structural and chemical features of interfacial sites are developed to weigh their relative importance and contributions to segregation tendency, revealing a significant increase in predictive capability when including local chemical information. Overall, this work, highlighting the complex interplay between local grain boundary structure and chemical short-range ordering, suggest tunable segregation and chemical ordering by tailoring grain boundary structure in multi-principal element alloys.

cond-mat.mtrl-sci↗

arXiv2022

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material's atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterisation techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterisation, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry (DSC) of irradiated Ti measures defect densities 5 times greater than those determined using transmission electron microscopy (TEM). Our experiments also reveal two energetically-distinct processes where the established annealing model predicts one. Molecular dynamics (MD) simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterisation techniques, and has the potential to uncover new mechanisms behind the evolution of defects in materials.

cond-mat.mtrl-sci↗

arXiv2022

Maximum strength and dislocation patterning in multi-principal element alloys

Multi-principal element alloys (MPEAs), commonly termed as medium- or high-entropy alloys containing three or more components in high concentrations, render a tunable chemical short-range order (SRO). Leveraging large-scale atomistic simulations, we probe the limit of Hall-Petch strengthening and deformation mechanisms in a model CrCoNi alloy and unravel chemical short-range ordering effects. It is found that, in the presence of SRO, the maximum strength is appreciably increased, and the strongest grain size drifts to a small value. Additionally, the propensity for faulting and deformation transformation is reduced and accompanied by the intensification of planar slip and strain localization. We reveal strikingly different deformation microstructures and dislocation patterns that prominently depend on crystallographic grain orientation and the number of slip planes activated during deformation. Grain of single planar slip attains the highest volume fraction of deformation-induced structure transformation, and grain with double active slip planes develops the densest dislocation network. These results advancing the fundamental understanding of deformation mechanisms and dislocation patterning in MPEAs suggest a mechanistic strategy for tuning mechanical behavior through simultaneously tailoring grain texture and local chemical order.

cond-mat.mtrl-sci↗

arXiv2022

Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. However, how to efficiently identify optimal compositions from such a large design space for targeted properties is a grand challenge in material science. Here we developed a convolutional neural network (CNN) model that can accurately and efficiently predict path-dependent vacancy migration energy barriers, which are critical to diffusion behaviors and many high-temperature properties, of MPEAs at any compositions and with different chemical short-range orders within a given alloy system. The success of the CNN model makes it promising for developing a database of diffusion barriers for different MPEA systems, which would accelerate alloy screening for the discovery of new compositions with desirable properties. Besides, the length scale of local configurations relevant to migration energy barriers is uncovered, and the implications of this success to other aspects of materials science are discussed.

cond-mat.mtrl-sci↗