Computational Materials Science最新文献

{"title":"Vacancy diffusion in non-stoichiometric ε-WB2-x","authors":"S.S. Setayandeh ,&nbsp;M.I. Brand ,&nbsp;A.H. Khan ,&nbsp;E.G. Obbard ,&nbsp;J.O. Astbury ,&nbsp;C.L. Wilson ,&nbsp;S. Irukuvarghula ,&nbsp;P.A. Burr","doi":"10.1016/j.commatsci.2025.113978","DOIUrl":"10.1016/j.commatsci.2025.113978","url":null,"abstract":"<div><div>The diffusion of boron (V_B) and tungsten vacancies (V_W) in the hypo-stoichiometric ε-phase of tungsten boride was studied using an Atomic (Lattice) Kinetic Monte Carlo (AKMC) approach, informed by Density Functional Theory (DFT) simulations. To account for the hypo-stoichiometric nature of the ε-phase, two limiting compositions, B-poor and B-rich, were considered. Results showed that both V_B and V_W exhibit strong anisotropic behaviour, with basal migration requiring significantly less energy than c-axis migration. Consequently, diffusion coefficients for both vacancies are orders of magnitude smaller in the c-direction regardless of B stoichiometry, indicating a predominance of 2D diffusion. This behaviour impacts the evolution of radiation-induced defects, potentially leading to anisotropic swelling. While the basal diffusivity of V_B remains largely unaffected by the stoichiometry, its c-axis diffusivity is found to be highly sensitive to boron content, which enhances vacancy migration through additional pathways. Although diffusion is found to be faster in the basal planes, increased boron occupancy slightly reduces the level of anisotropy, a trend that also diminishes at higher temperatures. Nonetheless, the V_B diffusion remains significantly anisotropic, exceeding a factor of 100 even at extreme temperatures. These findings underscore the critical role of stoichiometry in regulating vacancy behaviour and promoting densification, which is essential for optimizing tungsten boride materials in compact fusion reactor applications.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113978"},"PeriodicalIF":3.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liquid structure of SmFe12-based alloys","authors":"Kengo Nishio ,&nbsp;Takehide Miyazaki ,&nbsp;Taro Fukazawa ,&nbsp;Tetsuya Fukushima ,&nbsp;Takashi Miyake","doi":"10.1016/j.commatsci.2025.113947","DOIUrl":"10.1016/j.commatsci.2025.113947","url":null,"abstract":"<div><div>Annealing of SmFe₁₂-based glasses is a potentially effective approach for producing ThMn₁₂-type crystals, which are promising materials for main phases of next-generation strong magnets. However, preparing these glasses is difficult. Glasses are typically made from their melt through rapid cooling. In this paper, we study the liquid structure of SmFe₁₂-based alloys by using <em>ab initio</em> molecular dynamics simulations and the <span><math><msub><mrow><mi>p</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span> code, as well as by developing a hard-sphere model of liquid SmFe₁₂. Our simulation results, combined with previous experimental observations, elucidate the glass-forming ability of SmFe₁₂-based alloys as follows. Two factors make it difficult to vitrify liquid SmFe₁₂: one is its structural similarity to liquid Fe which is known as a poor glass former; The other is chemical inhomogeneity. Clusters free of Sm atoms form in liquid SmFe₁₂ due to excluded volume effects, facilitating the nucleation of Fe crystals and thereby preventing glass formation. Doping liquid SmFe₁₂ with titanium reduces the structural similarity but increases Sm-free clusters. However, because the reduced structural similarity has a dominant effect, the overall glass-forming ability of SmFe₁₂ increases with titanium doping. Cobalt doping would also improve the glass-forming ability due to the reduced structural similarity and the decreased Sm-free clusters. If a glass is successfully made from a cobalt-doped SmFe₁₂ liquid, it would contain fewer Sm-free clusters than a pure SmFe₁₂ glass. The lower content of Sm-free clusters would facilitate the formation of ThMn₁₂-type crystals through annealing of the glass.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113947"},"PeriodicalIF":3.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A quantum-transformer hybrid architecture for polymer property prediction: Addressing data sparsity issues","authors":"Aodong Zhang ,&nbsp;Chengke Bao ,&nbsp;Zhanbo Zhu ,&nbsp;Weidong Ji","doi":"10.1016/j.commatsci.2025.113950","DOIUrl":"10.1016/j.commatsci.2025.113950","url":null,"abstract":"<div><div>Polymers have been used in various applications, and accurate prediction of polymer properties is important for their application and design. Although machine learning has demonstrated excellent performance, existing models still have limitations in dealing with complex nonlinear relationships and sparse datasets. This study proposes a novel solution - a PolyQT model that combines quantum neural networks (QNNs) with the Transformer architecture. The model aims to take advantage of quantum computing to enhance the modeling capability of complex nonlinear relationships while alleviating the data sparsity problem and improving the prediction accuracy of polymer features through its special structural design. Prediction experiments for six key features, namely ionization energy, dielectric constant, glass transition temperature, refractive index, crystallization trend and polymer density,show the significant advantages of the model: on the complete dataset, the R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of ionization energy, dielectric constant, glass transition temperature, refractive index reach and polymer density 0.85, 0.77, 0.85, 0.83 and 0.92, respectively, which are better than those of all the benchmark models; and the R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of crystallization trend is 0.27, which is not worse than that of most of the benchmark models. The R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of the PolyQT model is always better than that of the classical Transformer under different data sparsity conditions, such as 40%, 60%, and 80%, indicating its superiority in sparse data processing. In addition, by comparing experiments with different numbers of quantum bits, we find that the model performs best with eight quantum bits, further exploring the critical role of the quantum mechanism in the model. Although quantum computing technology is still evolving, this study highlights the potential of quantum mechanics in predicting polymer properties, offers new insights for further understanding and optimizing these properties, and suggests promising directions for interdisciplinary research.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113950"},"PeriodicalIF":3.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of 3D grain size distributions from 2D sections in real and simulated microstructures","authors":"Thomas van der Jagt ,&nbsp;Martina Vittorietti ,&nbsp;Karo Sedighiani ,&nbsp;Cornelis Bos ,&nbsp;Geurt Jongbloed","doi":"10.1016/j.commatsci.2025.113949","DOIUrl":"10.1016/j.commatsci.2025.113949","url":null,"abstract":"<div><div>Obtaining information about the 3D grain size distribution of metallic microstructures is crucial for understanding the mechanical behavior of metals. This paper addresses the problem of estimating the 3D grain size distribution from 2D cross sections. This is a well-known stereological problem and different estimators have been proposed in the literature. We propose a statistical estimation procedure that provides consistent estimates without relying on arbitrary binning choices. When applying this procedure to space filling structures, we investigate the impact of the choice of grain shape and propose a heuristic to choose the best grain shape. To validate our approach, we employ simulations using Laguerre–Voronoi diagrams and apply our methodology to a sample of Interstitial-Free steel, obtained via EBSD.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113949"},"PeriodicalIF":3.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphological evolution of γ' and γ'' precipitation in a model superalloy: Insights from 3D phase-field simulations","authors":"Chan Wang ,&nbsp;Muhammad Umair ,&nbsp;Yuxun Jiang ,&nbsp;Dhanunjaya K. Nerella ,&nbsp;Muhammad Adil Ali ,&nbsp;Ingo Steinbach","doi":"10.1016/j.commatsci.2025.113972","DOIUrl":"10.1016/j.commatsci.2025.113972","url":null,"abstract":"<div><div>This study explores the role of nucleation conditions of γ'' and γ' strengthening phases in determining the microstructural characteristics of Ni-based superalloys. A 3D phase-field model is employed to investigate the competitive growth behavior of these phases under aging conditions at 850 K. The analysis reveals that the initial nucleation conditions significantly affect the equilibrium phase morphology, including size dispersion and spatial distribution, while the final equilibrium volume fractions remain constant. Equal initial nucleation densities of γ'' and γ' phases promote a more uniform spatial distribution, reduced size dispersion, and decreased von Mises stress, leading to improved precipitation strengthening. This is particularly important, as both precipitate phases show an opposite sign of the misfit compared to the matrix. This leads to a minimum state of elastic energy for an even distribution of precipitates in an alternating setting and allows for tuning of the equilibrium fraction, constrained by elastic interaction. These findings highlight the importance of optimizing preferential nucleation to enhance the microstructure and properties of Ni-based superalloys.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113972"},"PeriodicalIF":3.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New descriptors of connectivity-bottleneck effects improve understanding and prediction of diffusive transport in pore geometries","authors":"Sandra Barman ,&nbsp;Holger Rootzén ,&nbsp;David Bolin","doi":"10.1016/j.commatsci.2025.113942","DOIUrl":"10.1016/j.commatsci.2025.113942","url":null,"abstract":"<div><div>Bottlenecks can drastically reduce transport through porous materials. Previous work has concentrated on constriction-bottlenecks caused by variations in pore size. Here we study connectivity-bottlenecks, which are caused by many paths in the pore network passing through the same small part of the material. We develop three new connectivity descriptors, geodesic channel-strength, pore size-channels, and the closed pore-tortuosity that capture these effects.</div><div>Five sets of computer-generated pore geometries with a wide variation in characteristics were used to evaluate the effect bottlenecks have on diffusive transport. We show that low connectivity as measured by the new bottleneck descriptors, can decrease diffusive transport drastically, but that in these data sets constriction-bottlenecks had a smaller effect. We also show that path-lengths and connectivity-bottlenecks can be highly correlated and adjustments using theoretical models of diffusive transport can help separate the effects. We provide a freely available software MIST that can be used to measure connectivity-bottleneck effects.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113942"},"PeriodicalIF":3.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the microstructure–property relationship in polymer foams using advanced statistical methods, machine learning and deep learning: A review","authors":"Dawid Walicki ,&nbsp;Paweł Zawistowski ,&nbsp;Joanna Ryszkowska","doi":"10.1016/j.commatsci.2025.113909","DOIUrl":"10.1016/j.commatsci.2025.113909","url":null,"abstract":"<div><div>Valuable information embedded within the microstructure of foam polymers should be utilized to further develop this group of materials and to tailor their properties for specific applications. Statistics, machine learning and deep learning methods could support scientists in detecting complex patterns in data and automating certain tasks. Such methods can be applied to characterize both the parameters of the microstructure and the interdependencies among them, as well as to predict macroscopic foam properties. Datasets can be sourced from both real images of synthesized materials and high-throughput simulations. This comprehensive review investigates the applications of mentioned methods in porous materials research. It delineates which microstructural features can be accurately quantified through 2D image analysis and identifies those that require 3D imaging. This paper covers the most common model architectures, training processes, sets of hyperparameters, the size of training datasets, the ability of models to generalize to other materials, the importance of explainability in models’ decisions, and current limitations. By highlighting these aspects, the review provides valuable insights that can guide future research in the field. The article also discusses future research directions by presenting underexplored model architectures.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113909"},"PeriodicalIF":3.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molybdenum segregation at grain boundaries in a nanograined Ni-Mo alloy: Implications for yielding behavior and plastic deformation modes","authors":"Sihan Hao ,&nbsp;Jiaxiang Li ,&nbsp;Kenta Yamanaka ,&nbsp;Akihiko Chiba","doi":"10.1016/j.commatsci.2025.113973","DOIUrl":"10.1016/j.commatsci.2025.113973","url":null,"abstract":"<div><div>Solute segregation at grain boundaries (GBs) significantly modifies GB characteristics and influences the macroscopic properties of nanograined polycrystals. This study demonstrates a substantial impact of Mo segregation at GBs on the GB characteristics, yielding behavior, and plastic deformation modes in a nanograined Ni-Mo alloy. Atomic segregation simulations reveal that Mo atoms primarily occupy tensile stress sites at amorphous GBs without substantially altering site volume. However, Mo atoms at tensile stress sites compress atomic volumes at compressive stress sites, thereby increasing compressive stress. Consequently, overall GB atomic volume decreases while GB atomic compressive stress increases. Tensile deformation simulations indicate that dislocation emission from GBs is inhibited as the fraction of Mo atoms at GBs increases. The decreased GB energy and atomic volume, along with increased atomic compressive stress, are indicative of the inhibition of dislocation emission due to Mo segregation. When the excess Mo concentration reaches 2.9 at.%, nanograin boundary relaxation is induced, mitigating nanograin coarsening and softening.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113973"},"PeriodicalIF":3.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Ni-based single crystal superalloys by machine learning based on data-driven multi-task optimization","authors":"Lixian Lian ,&nbsp;Wenjing Li ,&nbsp;Yu Zhang ,&nbsp;Xiufang Gong ,&nbsp;Wang Hu ,&nbsp;Ying Liu","doi":"10.1016/j.commatsci.2025.113969","DOIUrl":"10.1016/j.commatsci.2025.113969","url":null,"abstract":"<div><div>In order to ascertain the requirements of high-quality gas turbine components, an investigation was undertaken into the composition design of Ni-based single crystal superalloys utilising high-throughput, data-driven machine learning methodologies. A characteristic parameter space influencing high-temperature strength in superalloys was established through theoretical calculation and the extraction of data from the superalloy manual. By means of correlation screening, it was determined that Vγ′、V_TCP and Tγ′-solvus represent the crucial parameters, which were used as the basis for optimisation tasks. In accordance with the principles of domain knowledge, a series of constraints were established for the purpose of screening the prospective alloy components, with the objective of achieving a high level of temperature strength. Subsequently, the preferred composition alloys and the commercial alloys were prepared through experimentation. Characterisation of the new composition alloys revealed that they exhibited a microstructure with 60 ∼ 75 % cubic γ′ phase, which demonstrated a relatively strong correlation with the optimisation tasks. Furthermore, the results show that the new alloys exhibit not only superior high-temperature strength but also notable improvements in elongation, density and cost, conferring them with enhanced economic value and potential for wider application compared to the current commercial alloys. The correlation between microstructure and properties suggests that the enhancement in performance is attributable to the optimisation of key microstructure objectives, offering a promising avenue for alloy design.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113969"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method","authors":"Pin Wu ,&nbsp;Haiwang Huang ,&nbsp;Qingcheng Yang ,&nbsp;Bo Qian ,&nbsp;Yongxin Gao ,&nbsp;Yiguo Yang ,&nbsp;Huiran Zhang ,&nbsp;Qiang Zhen","doi":"10.1016/j.commatsci.2025.113883","DOIUrl":"10.1016/j.commatsci.2025.113883","url":null,"abstract":"<div><div>This study introduces SimGate, a novel deep learning surrogate model for predicting microstructure evolution using the phase-field method. Combining the temporal modeling capabilities of “Simpler yet better video prediction (SimVP)” with the multi-order aggregation features of “Multi-order gated aggregation network (MogaNet)”, SimGate leverages robust temporal dynamics alongside spatial and channel aggregation modules to ensure precise detail capture and spatial consistency. To demonstrate SimGate’s ability to tackle challenging scenarios, high-temperature sintering simulations of polycrystalline cerium dioxide (CeO₂) particles were selected as a test case. These simulations, chosen for their complexity, involve both Cahn–Hilliard-type and Allen–Cahn-type phase-field equations along with intricate interfacial dynamics, and they were validated through experimental data. SimGate accurately predicts the sintering process from limited initial time steps and exhibits strong extrapolation capabilities in modeling unseen microstructures over extended time scales. Compared to traditional phase-field simulations, which require hours per case, SimGate reduces computational time to seconds while maintaining a prediction accuracy of around 90%. Additionally, point-wise error analysis shows that the average accuracy is improved by 7.80% and 12.41% compared with the original SimVP and well-known Long Short-Term Memory Networks (LSTM), respectively. An ablation analysis was performed to reveal the contributions of key components in the proposed SimGate framework. By significantly enhancing computational efficiency and accuracy, SimGate demonstrates broad potential as a generalizable microstructure prediction model applicable to diverse material and mechanical processing scenarios beyond sintering.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"256 ","pages":"Article 113883"},"PeriodicalIF":3.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}