Pub Date : 2026-03-11DOI: 10.1016/j.actamat.2026.122114
Charles Manière, Fatima Hammoud
The Finite Element Method (FEM) is well suited to capture the complex sintering behavior of 3D-printed parts, including anisotropy, shrinkage, and temperature-dependent porous deformation. However, applying FEM directly to real parts is challenging, as intricate geometries require highly refined meshes, leading to long computation times and frequent divergence from stress concentrations. In this work, we circumvent these inherent FEM limitations by combining FEM simulations with deep learning. Instead of directly simulating the entire part, a large parametric study of overhang bar geometries under various loading conditions is performed to generate a synthetic dataset of 105 data points. This dataset is used to train a deep learning model capable of predicting the sintering distortion risk of printed parts. A key originality of this approach lies in using elastic FEM simulations as descriptors of the mechanical solicitation of the part, combined with the sintering work as a thermal process descriptor. This hybrid methodology is highly efficient: a simple elastic FEM calculation of a 3D part, coupled with a forward prediction by the trained neural network (few seconds) can accurately determine whether a part will withstand sintering or if additional supports are required.
{"title":"Hybrid FEM-Deep Learning Framework for Robust Prediction of Sintering Distortions in 3D-Printed Parts","authors":"Charles Manière, Fatima Hammoud","doi":"10.1016/j.actamat.2026.122114","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122114","url":null,"abstract":"The Finite Element Method (FEM) is well suited to capture the complex sintering behavior of 3D-printed parts, including anisotropy, shrinkage, and temperature-dependent porous deformation. However, applying FEM directly to real parts is challenging, as intricate geometries require highly refined meshes, leading to long computation times and frequent divergence from stress concentrations. In this work, we circumvent these inherent FEM limitations by combining FEM simulations with deep learning. Instead of directly simulating the entire part, a large parametric study of overhang bar geometries under various loading conditions is performed to generate a synthetic dataset of 10<sup>5</sup> data points. This dataset is used to train a deep learning model capable of predicting the sintering distortion risk of printed parts. A key originality of this approach lies in using elastic FEM simulations as descriptors of the mechanical solicitation of the part, combined with the sintering work as a thermal process descriptor. This hybrid methodology is highly efficient: a simple elastic FEM calculation of a 3D part, coupled with a forward prediction by the trained neural network (few seconds) can accurately determine whether a part will withstand sintering or if additional supports are required.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"8 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-11DOI: 10.1016/j.actamat.2026.122107
Alexander Campos-Quiros, Animesh Kundu, Masashi Watanabe
The segregation of dopants at grain boundaries (GB) affects the bulk mechanical and electrical properties, as well as the microstructure evolution in polycrystalline materials. The structure and atomic arrangements at GBs play a crucial role in dictating the segregation behaviors. However, the effect of different GB arrangements within a single GB on the segregation behavior is not well understood yet. For this reason, a near-Σ3 twist boundary was fabricated to introduce GB-site anisotropy using magnesium aluminate spinel single crystals doped with yttrium (Y). The small deviation angle from the exact Σ3 configuration introduced a network of secondary GB dislocations identified as Shockley partials (1/6<112>). Detailed atomic-resolution imaging on conventional cross-sectional projection, as well as a pseudo-plan-view projection, was carried out to directly observe the Y segregation. Three distinct Y segregation behaviors were identified within a single GB: ordered (between dislocation network), disorder (near dislocations), and negligible Y segregation (remaining regions between the dislocation network). Atomic-resolution imaging and atomic configuration models of the GB revealed that the distinct segregation behaviors were correlated to changes in the local atomic configurations, especially the arrangement of O atoms. The ordered Y segregation was spatially correlated to coherent and symmetric segments of the boundary with larger excess free volumes. The regions with no Y segregation presented an O atom arrangement similar to that in the bulk lattice, where the limited free volume hindered the Y segregation. These results provided experimental evidence of how different atomic arrangements led to distinct segregation behaviors within a single GB.
{"title":"Atomic-Scale Observations of Distinct Segregation Behaviors Driven by Site Anisotropy in a near-Σ3 Grain Boundary","authors":"Alexander Campos-Quiros, Animesh Kundu, Masashi Watanabe","doi":"10.1016/j.actamat.2026.122107","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122107","url":null,"abstract":"The segregation of dopants at grain boundaries (GB) affects the bulk mechanical and electrical properties, as well as the microstructure evolution in polycrystalline materials. The structure and atomic arrangements at GBs play a crucial role in dictating the segregation behaviors. However, the effect of different GB arrangements within a single GB on the segregation behavior is not well understood yet. For this reason, a near-Σ3 twist boundary was fabricated to introduce GB-site anisotropy using magnesium aluminate spinel single crystals doped with yttrium (Y). The small deviation angle from the exact Σ3 configuration introduced a network of secondary GB dislocations identified as Shockley partials (1/6<112>). Detailed atomic-resolution imaging on conventional cross-sectional projection, as well as a pseudo-plan-view projection, was carried out to directly observe the Y segregation. Three distinct Y segregation behaviors were identified within a single GB: ordered (between dislocation network), disorder (near dislocations), and negligible Y segregation (remaining regions between the dislocation network). Atomic-resolution imaging and atomic configuration models of the GB revealed that the distinct segregation behaviors were correlated to changes in the local atomic configurations, especially the arrangement of O atoms. The ordered Y segregation was spatially correlated to coherent and symmetric segments of the boundary with larger excess free volumes. The regions with no Y segregation presented an O atom arrangement similar to that in the bulk lattice, where the limited free volume hindered the Y segregation. These results provided experimental evidence of how different atomic arrangements led to distinct segregation behaviors within a single GB.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"9 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1016/j.actamat.2026.122066
Himanshu Joshi, Ian Chesser, Brandon Runnels, Nikhil Chandra Admal
Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of [001] symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by partial disconnections, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure.
{"title":"Energetics of the nucleation and glide of disconnection modes in symmetric tilt grain boundaries","authors":"Himanshu Joshi, Ian Chesser, Brandon Runnels, Nikhil Chandra Admal","doi":"10.1016/j.actamat.2026.122066","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122066","url":null,"abstract":"Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of <mml:math altimg=\"si143.svg\" display=\"inline\"><mml:mrow><mml:mo>[</mml:mo><mml:mn>0</mml:mn><mml:mspace width=\"0.16667em\"></mml:mspace><mml:mn>0</mml:mn><mml:mspace width=\"0.16667em\"></mml:mspace><mml:mn>1</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:math> symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by <ce:italic>partial disconnections</ce:italic>, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"32 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147392374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metallic glasses (MGs), free from strict stoichiometry constraints, usually exhibit far greater compositional flexibility than their crystalline intermetallic counterparts. Based on a simplified picture, a near-linear or monotonic dependence of some properties on composition is often assumed and experimentally observed over a relatively broad composition range in MGs, seemingly following the rule of mixtures. The deviation from this simple compositional trend is also common in MGs, which is critical to understanding the complexity of glasses. Here, we uncover an abrupt transition in the compositional dependence of properties in the Ce65Al35-xCox (at.%, 5 ≤ x ≤ 30) MGs at a critical composition x = 17.5. The transition is consistently detected by a set of complementary experimental techniques, including synchrotron x-ray diffraction, in situ high/low-temperature electric resistance measurements, differential scanning calorimetry, and nanoindentation tests. This unusual transition suggests complex, composition-dependent interactions among constituent elements in MGs. Synchrotron x-ray absorption spectroscopy and nuclear magnetic resonance spectroscopy further reveal a crossover in 4f electron states and bonding characteristics as x varies. These findings highlight the critical roles of composition-dependent electronic structures and chemical interactions, beyond the classical random hard-sphere model, in dictating the physical properties of MGs, providing new insight into the elusive composition-structure-property relationships and guiding the design of MGs with tailored properties.
{"title":"Composition-dependent electronic hybridization induced transitions in metallic glasses","authors":"Fujun Lan, Ziliang Yin, Hongbo Lou, Xin Zhang, Lianghua Xiong, Di Peng, Dazhe Xu, Chenjie Lou, Tao Liang, Mingxue Tang, Hongwei Sheng, Zhidan Zeng, Qiaoshi Zeng","doi":"10.1016/j.actamat.2026.122100","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122100","url":null,"abstract":"Metallic glasses (MGs), free from strict stoichiometry constraints, usually exhibit far greater compositional flexibility than their crystalline intermetallic counterparts. Based on a simplified picture, a near-linear or monotonic dependence of some properties on composition is often assumed and experimentally observed over a relatively broad composition range in MGs, seemingly following the rule of mixtures. The deviation from this simple compositional trend is also common in MGs, which is critical to understanding the complexity of glasses. Here, we uncover an abrupt transition in the compositional dependence of properties in the Ce<sub>65</sub>Al<sub>35</sub>-<em><sub>x</sub></em>Co<em><sub>x</sub></em> (at.%, 5 ≤ <em>x</em> ≤ 30) MGs at a critical composition <em>x =</em> 17.5. The transition is consistently detected by a set of complementary experimental techniques, including synchrotron x-ray diffraction, <em>in situ</em> high/low-temperature electric resistance measurements, differential scanning calorimetry, and nanoindentation tests. This unusual transition suggests complex, composition-dependent interactions among constituent elements in MGs. Synchrotron x-ray absorption spectroscopy and nuclear magnetic resonance spectroscopy further reveal a crossover in 4<em>f</em> electron states and bonding characteristics as <em>x</em> varies. These findings highlight the critical roles of composition-dependent electronic structures and chemical interactions, beyond the classical random hard-sphere model, in dictating the physical properties of MGs, providing new insight into the elusive composition-structure-property relationships and guiding the design of MGs with tailored properties.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"15 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1016/j.actamat.2026.122101
Jeong Ah Lee, Yeon Woo Kim, Takayoushi Nakano, Hyomoon Joo, Jeong Min Park, Hyoung Seop Kim
The yield strength of laser powder bed fusion (LPBF) alloys remains challenging to predict owing to defect-driven variability that cannot be captured by conventional near-dense empirical equations. Here, we develop a data-selective machine learning (DSML) pipeline that integrates data-driven black-box modeling with physics-informed white-box modeling through symbolic regression to derive a defect-aware, interpretable closed-form equation. A dual-source dataset was constructed, including 44 fully labeled datasets (process parameters, microstructural features, and mechanical properties), and 111 process-only datasets containing porosity data. The DSML framework identifies critical descriptors and then embeds a porosity sub-model into a closed-form yield-strength equation, explicitly capturing the influence of process-induced defects. Validation was performed via AlSi10Mg fabricated using LPBF under five distinct conditions. The results revealed that the porosity-aware white-box model achieves a coefficient of determination of 0.90 and mean absolute error (MAE) of 9.51 MPa, outperforming both the black-box predictor and a widely used cell-size–based empirical relation (MAE = 41.98 MPa). The recovered terms align with known mechanisms (effective load-bearing reduction by pores and boundary-mediated strengthening) and preserve dimensional consistency, enabling the construction of process–design maps for defect-aware optimization. By internalizing defect effects in an interpretable equation and performing rigorous validation against independent experimental conditions, this work provides a reproducible, physics-consistent route to determining process–structure–property relationships for LPBF AlSi10Mg and a scalable foundation for incorporating additional strengthening mechanisms in next-generation LPBF materials.
{"title":"Data-Selective Machine Learning Framework (DSML) for Defect-Aware, Interpretable Yield-Strength Prediction for LPBF-Fabricated AlSi10Mg Alloys","authors":"Jeong Ah Lee, Yeon Woo Kim, Takayoushi Nakano, Hyomoon Joo, Jeong Min Park, Hyoung Seop Kim","doi":"10.1016/j.actamat.2026.122101","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122101","url":null,"abstract":"The yield strength of laser powder bed fusion (LPBF) alloys remains challenging to predict owing to defect-driven variability that cannot be captured by conventional near-dense empirical equations. Here, we develop a data-selective machine learning (DSML) pipeline that integrates data-driven black-box modeling with physics-informed white-box modeling through symbolic regression to derive a defect-aware, interpretable closed-form equation. A dual-source dataset was constructed, including 44 fully labeled datasets (process parameters, microstructural features, and mechanical properties), and 111 process-only datasets containing porosity data. The DSML framework identifies critical descriptors and then embeds a porosity sub-model into a closed-form yield-strength equation, explicitly capturing the influence of process-induced defects. Validation was performed via AlSi10Mg fabricated using LPBF under five distinct conditions. The results revealed that the porosity-aware white-box model achieves a coefficient of determination of 0.90 and mean absolute error (MAE) of 9.51 MPa, outperforming both the black-box predictor and a widely used cell-size–based empirical relation (MAE = 41.98 MPa). The recovered terms align with known mechanisms (effective load-bearing reduction by pores and boundary-mediated strengthening) and preserve dimensional consistency, enabling the construction of process–design maps for defect-aware optimization. By internalizing defect effects in an interpretable equation and performing rigorous validation against independent experimental conditions, this work provides a reproducible, physics-consistent route to determining process–structure–property relationships for LPBF AlSi10Mg and a scalable foundation for incorporating additional strengthening mechanisms in next-generation LPBF materials.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"1 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147392373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1016/j.actamat.2026.122097
Shilei Liu, Victoria Kaban, Victor T. Witusiewicz, Ivan Kaban
Phase competition in AlCoCrFeNix high-entropy alloys (x = 2.0, 2.1, 2.2, and 2.4) was investigated using synchrotron X-ray diffraction combined with electromagnetic levitation. In undercooled melts with x = 2.0, 2.1, and 2.2, the ordered B2 phase forms first via the transformation L → B2, marking the initial stage of solidification. This observation aligns well with the double recalescence phenomenon, where B2 crystallizes directly from the liquid, followed by the formation of a B2/A1 eutectic structure from the mushy zone, as captured in high-speed video recordings of the AlCoCrFeNi2.1 sample. In contrast, the AlCoCrFeNi2.4 alloy solidifies initially with the primary A1 phase via L → A1. The delay time (Δt) between nucleation of the primary phase and onset of eutectic transformation is strongly influenced by the nickel concentration. Moreover, the dissolution temperature of the ordered L12 phase during solid-state heating increases steadily with rising nickel content. These experimental findings show strong agreement with phase evolution predictions obtained through thermodynamic modeling of the AlCoCrFeNi system using Thermo-Calc software. Overall, this study provides a comprehensive picture of the phase formation and stability, solidification kinetics, and microstructural development of AlCoCrFeNix high-entropy alloys.
{"title":"In situ synchrotron X-ray diffraction revealing competition between A1 and B2 phases in AlCoCrFeNix high-entropy alloys","authors":"Shilei Liu, Victoria Kaban, Victor T. Witusiewicz, Ivan Kaban","doi":"10.1016/j.actamat.2026.122097","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122097","url":null,"abstract":"Phase competition in AlCoCrFeNi<ce:italic><ce:inf loc=\"post\">x</ce:inf></ce:italic> high-entropy alloys (<ce:italic>x</ce:italic> = 2.0, 2.1, 2.2, and 2.4) was investigated using synchrotron X-ray diffraction combined with electromagnetic levitation. In undercooled melts with <ce:italic>x</ce:italic> = 2.0, 2.1, and 2.2, the ordered B2 phase forms first via the transformation L → B2, marking the initial stage of solidification. This observation aligns well with the double recalescence phenomenon, where B2 crystallizes directly from the liquid, followed by the formation of a B2/A1 eutectic structure from the mushy zone, as captured in high-speed video recordings of the AlCoCrFeNi<ce:inf loc=\"post\">2.1</ce:inf> sample. In contrast, the AlCoCrFeNi<ce:inf loc=\"post\">2.4</ce:inf> alloy solidifies initially with the primary A1 phase via L → A1. The delay time (Δ<ce:italic>t</ce:italic>) between nucleation of the primary phase and onset of eutectic transformation is strongly influenced by the nickel concentration. Moreover, the dissolution temperature of the ordered L1<ce:inf loc=\"post\">2</ce:inf> phase during solid-state heating increases steadily with rising nickel content. These experimental findings show strong agreement with phase evolution predictions obtained through thermodynamic modeling of the AlCoCrFeNi system using Thermo-Calc software. Overall, this study provides a comprehensive picture of the phase formation and stability, solidification kinetics, and microstructural development of AlCoCrFeNi<ce:italic><ce:inf loc=\"post\">x</ce:inf></ce:italic> high-entropy alloys.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"15 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147392377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1016/j.actamat.2026.122094
Guilherme Victor Selicani, Andrea Roberto Insinga, Astri Bjørnetun Haugen, James Roscow
Recent advances in manufacturing have enabled the realization of engineered piezoelectric architectures with enhanced functionality and performance, thereby underpinning a new generation of ferroelectric ceramic-based energy harvesters, hydrophones, and precision actuators. This work presents a study of ferroelectric polarization effects in piezoelectric ceramic composite architectures enabled by modern manufacturing methods. A simplified modelling framework is introduced to investigate these effects in porous and lattice structures, including heterogeneities in the ferroelectric ceramic phase, path dependence associated with sequential poling strategies, and the use of corona poling and embedded electrodes. Within this framework, representative volume elements (RVEs) of piezoelectric composites are studied using nonlinear poling simulations based on a semi-microscopic Jiles–Atherton model, followed by a homogenization step. Different methods are evaluated to study variations in piezoceramic properties with remanent polarization and are compared with established, experimentally validated approaches. The nonlinear formulation entails higher numerical cost and is best suited for RVEs exhibiting strong poling-field gradients arising from geometric effects or electric-field path dependence during poling. Material failure is qualitatively assessed, indicating that sharp polarization gradients may induce localized, poling-related stress concentrations in additively manufactured piezoelectric structures. These results highlight that polarization and actuation strategies dictated by electrode architecture must be considered during design, as they can significantly alter the effective piezoelectric tensor of the composite.
{"title":"Numerical modelling framework for studying poling effects in architectured piezoelectric structures","authors":"Guilherme Victor Selicani, Andrea Roberto Insinga, Astri Bjørnetun Haugen, James Roscow","doi":"10.1016/j.actamat.2026.122094","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122094","url":null,"abstract":"Recent advances in manufacturing have enabled the realization of engineered piezoelectric architectures with enhanced functionality and performance, thereby underpinning a new generation of ferroelectric ceramic-based energy harvesters, hydrophones, and precision actuators. This work presents a study of ferroelectric polarization effects in piezoelectric ceramic composite architectures enabled by modern manufacturing methods. A simplified modelling framework is introduced to investigate these effects in porous and lattice structures, including heterogeneities in the ferroelectric ceramic phase, path dependence associated with sequential poling strategies, and the use of corona poling and embedded electrodes. Within this framework, representative volume elements (RVEs) of piezoelectric composites are studied using nonlinear poling simulations based on a semi-microscopic Jiles–Atherton model, followed by a homogenization step. Different methods are evaluated to study variations in piezoceramic properties with remanent polarization and are compared with established, experimentally validated approaches. The nonlinear formulation entails higher numerical cost and is best suited for RVEs exhibiting strong poling-field gradients arising from geometric effects or electric-field path dependence during poling. Material failure is qualitatively assessed, indicating that sharp polarization gradients may induce localized, poling-related stress concentrations in additively manufactured piezoelectric structures. These results highlight that polarization and actuation strategies dictated by electrode architecture must be considered during design, as they can significantly alter the effective piezoelectric tensor of the composite.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"55 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1016/j.actamat.2026.122096
Liu Qu, Jinyan Wang, Evangelos I. Papaioannou, Song Li, He Liu, Haitao Zhao, Gaowu Qin
Heat and ionic transport within the catalyst are critical features for highly endothermic or exothermic reactions, however, being poorly understood in terms of the impact on surface temperature and reaction kinetics. In this work, we combine experimental characterization with atomic-scale simulations to elucidate heat-transfer behavior and ionic diffusion in oxide catalysts. Zn-doped La₂Ce₂O₇ with a defect-fluorite structure, synthesized via coprecipitation, is employed as a model system. Microstructure, local ionic environments, and elemental distribution were analyzed using X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Thermal conductivity was studied through both molecular dynamics simulations and experimental measurements. The Zn-doping-dependent correlation between heat transport and ionic mobility is established, and we further elucidate heat transfer across the metal-oxide interface. These insights clarify the role of ionic doping in interfacial heat transport and provide guidance for tailoring catalyst thermodynamics to enhance catalytic activity.
{"title":"Heat Transport and Ion Diffusion Mechanisms in Zn-doped La2Ce2O7: A Combined Experimental and Simulation Study for Catalyst Design","authors":"Liu Qu, Jinyan Wang, Evangelos I. Papaioannou, Song Li, He Liu, Haitao Zhao, Gaowu Qin","doi":"10.1016/j.actamat.2026.122096","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122096","url":null,"abstract":"Heat and ionic transport within the catalyst are critical features for highly endothermic or exothermic reactions, however, being poorly understood in terms of the impact on surface temperature and reaction kinetics. In this work, we combine experimental characterization with atomic-scale simulations to elucidate heat-transfer behavior and ionic diffusion in oxide catalysts. Zn-doped La₂Ce₂O₇ with a defect-fluorite structure, synthesized via coprecipitation, is employed as a model system. Microstructure, local ionic environments, and elemental distribution were analyzed using X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Thermal conductivity was studied through both molecular dynamics simulations and experimental measurements. The Zn-doping-dependent correlation between heat transport and ionic mobility is established, and we further elucidate heat transfer across the metal-oxide interface. These insights clarify the role of ionic doping in interfacial heat transport and provide guidance for tailoring catalyst thermodynamics to enhance catalytic activity.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"15 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. To address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.
{"title":"Machine Learning Insights into Microstructural Origins of Transport and Mechanical Properties in Porous Microstructures","authors":"Longsheng Feng, Bo Wang, Sourav Chatterjee, Tae Wook Heo, Juergen Biener","doi":"10.1016/j.actamat.2026.122092","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122092","url":null,"abstract":"Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. To address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"89 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-07DOI: 10.1016/j.actamat.2026.122091
Dharmendra Pant, Suyash Varshney, Dilpuneet S. Aidhy
Balancing strength and ductility in body-centered cubic (bcc) refractory high entropy alloys remains a fundamental challenge due to the intrinsic trade-off between bond strength and dislocation mobility. Using density functional theory (DFT) calculations, we show that electronic structure and bond strength play a critical role in affecting both strength and ductility. Specifically, there is a noticeable decrease in the density of states at the Fermi level, N(Ef), within a short window of valence electron concentration (VEC) around 5.1 marking a crossover from metallic to covalent-type bonding in refractory bcc alloys. This change is driven by a disproportionate suppression of eg states, responsible for σ-type bonds along second nearest neighbor (2NN) directions, relative to t2g states. The resulting increase in 2NN stiffness outpaces the increase in the first nearest neighbor (1NN) stiffness and drives steep rise in Young and shear moduli, while simultaneously reducing local lattice distortion and ductility. Enrichment with higher-VEC Group VI elements (e.g., Cr, Mo, W) amplifies these effects, whereas addition of lower-VEC Group IV and V elements (e.g., Ti, Zr, Hf, V, Nb, Ta) reduces them. These results establish 2NN bonding as the dominant atomic-scale mechanism controlling the strength-ductility balance, providing a pathway for designing next-generation bcc refractory alloys.
{"title":"Dominant role of second nearest-neighbor bonding in strength-ductility tradeoff of bcc refractory high entropy alloys","authors":"Dharmendra Pant, Suyash Varshney, Dilpuneet S. Aidhy","doi":"10.1016/j.actamat.2026.122091","DOIUrl":"https://doi.org/10.1016/j.actamat.2026.122091","url":null,"abstract":"Balancing strength and ductility in body-centered cubic (bcc) refractory high entropy alloys remains a fundamental challenge due to the intrinsic trade-off between bond strength and dislocation mobility. Using density functional theory (DFT) calculations, we show that electronic structure and bond strength play a critical role in affecting both strength and ductility. Specifically, there is a noticeable decrease in the density of states at the Fermi level, N(<ce:italic>E<ce:inf loc=\"post\">f</ce:inf></ce:italic>), within a short window of valence electron concentration (VEC) around 5.1 marking a crossover from metallic to covalent-type bonding in refractory bcc alloys. This change is driven by a disproportionate suppression of <ce:italic>e<ce:inf loc=\"post\">g</ce:inf></ce:italic> states, responsible for σ-type bonds along second nearest neighbor (2NN) directions, relative to <ce:italic>t<ce:inf loc=\"post\">2g</ce:inf></ce:italic> states. The resulting increase in 2NN stiffness outpaces the increase in the first nearest neighbor (1NN) stiffness and drives steep rise in Young and shear moduli, while simultaneously reducing local lattice distortion and ductility. Enrichment with higher-VEC Group VI elements (e.g., Cr, Mo, W) amplifies these effects, whereas addition of lower-VEC Group IV and V elements (e.g., Ti, Zr, Hf, V, Nb, Ta) reduces them. These results establish 2NN bonding as the dominant atomic-scale mechanism controlling the strength-ductility balance, providing a pathway for designing next-generation bcc refractory alloys.","PeriodicalId":238,"journal":{"name":"Acta Materialia","volume":"414 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147392380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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