Computational Materials Science最新文献

{"title":"Layered double hydroxides reinforced epoxy composites: Computational analysis of microstructure effect on strength","authors":"Sigitas Kilikevičius ,&nbsp;Leon Mishnaevsky Jr. ,&nbsp;Daiva Zeleniakiene","doi":"10.1016/j.commatsci.2025.113816","DOIUrl":"10.1016/j.commatsci.2025.113816","url":null,"abstract":"<div><div>This paper analyses the mechanical and damage behaviour of epoxy composites incorporating magnesium–aluminium layered double hydroxides (LDH), which have potential applications as corrosion protective coatings. The analysis of these composites was carried out by developing a computational model based on numerical homogenisation approach, employing the micromechanical finite element method. The influence of the elastic modulus, aspect ratio and weight fractions of the LDH particles on the mechanical and damage behaviour of epoxy/LDH composites was investigated. Damage modelling was performed, capturing both crack formation and evolution. Damage mechanisms such as crack pinning and crack deflection due to the LDH particles were observed. The modelling demonstrated that with an increase in the weight fraction of LDH, the composite became stiffer and more brittle. Adding up to 5 wt% LDH particles to epoxy increased the elastic modulus of the composite by nearly 20%. The strain at break was reduced to 2 %. The model was validated against experimental data, demonstrating its ability to predict the behaviour of epoxy/LDH composites. The findings indicate that epoxy/LDH composites exhibit enhanced stiffness, making them suitable for practical applications as corrosion-protective coatings.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113816"},"PeriodicalIF":3.1,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrogen and water interactions with CrMnFeCoNi alloy from density functional theory calculations","authors":"Yichen Qian ,&nbsp;Artur Tamm ,&nbsp;David Cereceda ,&nbsp;ShinYoung Kang","doi":"10.1016/j.commatsci.2025.113789","DOIUrl":"10.1016/j.commatsci.2025.113789","url":null,"abstract":"<div><div>High entropy alloys (HEAs) are a promising class of materials with remarkable mechanical and catalytic properties. Among these, the quinary CrMnFeCoNi alloy (also called “Cantor alloy”) has attracted considerable attention given its thermodynamic stability and remarkable mechanical properties under different temperatures. Given that various degradation mechanisms involve multiple contaminants, such as hydrogen and water in hydrogen embrittlement and surface poisoning, respectively, understanding their interactions with the Cantor alloy is critical for its practical applications as structural, nuclear, or hydrogen storage material. In this work, we perform first-principles calculations based on Density Functional Theory (DFT) to investigate such interactions when considering various microstructures, including bulk materials and those containing certain defects, such as grain boundaries, stacking faults, and vacancies. We also employ Global Sensitivity Analysis to identify the importance of different factors in the stability of the impurities. We find that the accuracy of the H formation energy is significantly affected by spin polarization and chemical short-range order. The study also identifies a strong tendency for hydrogen interstitials to segregate to Σ5(210)/[001] symmetric tilt grain boundary, even when H concentrations are high, suggesting that a certain type of grain boundaries acts as H sinks within the alloy. This result is reinforced by the low formation energy of vacancy-hydrogen complexes, which can contain multiple hydrogen atoms. Finally, the surface reactivity analysis reveals that the adsorption energy of oxygen and hydroxyl groups is highly sensitive to the specific metal atom involved in the binding, with a clear preference for chromium atoms, which could have implications for the alloy’s oxidation and corrosion behavior.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113789"},"PeriodicalIF":3.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527128","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":"Performance assessment of high-throughput Gibbs free energy predictions of crystalline solids","authors":"Rasmus Fromsejer ,&nbsp;Bjørn Maribo-Mogensen ,&nbsp;Georgios M. Kontogeorgis ,&nbsp;Xiaodong Liang","doi":"10.1016/j.commatsci.2025.113770","DOIUrl":"10.1016/j.commatsci.2025.113770","url":null,"abstract":"<div><div>Crystalline solids are integral to a broad range of natural science and engineering applications and their Gibbs free energy <span><math><mi>G</mi></math></span> is an important parameter in modeling their thermodynamics. However, predicting <span><math><mi>G</mi></math></span> for solids remains a difficult task and an under-explored field in high-throughput thermochemistry. In this work, we benchmark the performance of the newest generation of machine learning (ML) predictions, machine learning interatomic potentials (MLIPs), and density functional theory in predicting <span><math><mi>G</mi></math></span> within the harmonic and quasi-harmonic approximations against experimental data from 100–2500 K and for up to 784 compounds. Furthermore, these calculations are fed to a reaction network (RN) from which experimentally informed predictions can be made. We find that predictions of <span><math><mi>G</mi></math></span> made by MLIPs display promising performance but with the help of the RN, simpler methods show similar or better performance. Nonetheless, we show that much of the calculated and experimental data for <span><math><mi>G</mi></math></span> still lack the accuracy and precision required for some thermodynamic modeling applications. Finally, we apply the RN to predict the room temperature Gibbs free energy of formation and find that it performs satisfactorily but that improvements need to be made before these predictions can be used as reliable indicators of thermodynamic stability in general applications.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113770"},"PeriodicalIF":3.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The atomic formation mechanism of GP zones in Al-Cu alloys: Insights from cluster expansion coupled with Monte Carlo simulation","authors":"Weiqi Fan ,&nbsp;Tongzhao Gong ,&nbsp;Weiye Hao ,&nbsp;Yun Chen ,&nbsp;Xing-Qiu Chen","doi":"10.1016/j.commatsci.2025.113798","DOIUrl":"10.1016/j.commatsci.2025.113798","url":null,"abstract":"<div><div>Guinier-Preston (GP) zones in Al-Cu alloys are noted for their precipitation-hardening effects and their critical role in elucidating the nanoscale organization of solute atoms. In this study, we employed the cluster expansion (CE) method combined with Monte Carlo (MC) simulations to investigate the formation and evolution of GP zones in Al-Cu alloys, particularly in the presence of vacancies and Mg. The CE model was trained on energies calculated by first-principles density functional theory (DFT), enabling subsequent MC simulations to explore clustering behavior of matrix-coherent Cu-rich structures. The simulations reproduced the formation of GPⅠ and GPⅡ zones, demonstrating that the model can capture atomic interactions responsible for Cu clustering. Crucially, the presence of vacancies promotes GP zone formation and facilitates the transition from GPⅠ to GPⅡ zones. Furthermore, Mg addition to Al-Cu alloys reduces the size of Cu-rich clusters while increasing shape diversity, and when combined with vacancies, leads to more complex structures consistent with experimentally observed Guinier-Preston-Bagaryatsky (GPB) zones. Subsequently, we studied GP zone decomposition during heating as a function of Cu concentration, revealing significantly improved agreement with experimental data compared to prior computational studies. These findings not only provide atomic-scale insights into GP zone formation mechanisms and the roles of vacancies and Mg, but also demonstrate the effectiveness of combining CE and MC approaches for studying nanoscale precipitation processes in Al-Cu and Al-Cu-Mg alloys.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113798"},"PeriodicalIF":3.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488808","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":"Finite-temperature atomistic and continuum stress fields of coherent precipitates with a small lattice misfit","authors":"Anas Abu-Odeh,&nbsp;James Warren","doi":"10.1016/j.commatsci.2025.113785","DOIUrl":"10.1016/j.commatsci.2025.113785","url":null,"abstract":"<div><div>An accurate description of elastic effects of coherent microstructures is necessary for the predictive modeling of microstructural evolution in many structural materials. To date, there has not been a demonstration on how continuum elasticity models are able to reproduce finite-temperature stress-fields and elastic energy estimates of coherent precipitates from atomistic simulations. We present a comparison of stress-fields of coherent precipitates in the body-centered cubic (BCC) Fe-Cr system obtained from atomistic simulation data and from continuum elasticity modeling. The magnitude and topology of the stress-fields show good agreement between the two approaches, and we show the importance of elastic effects on the Gibbs-Thompson effect for this small lattice misfit system. We conclude with a discussion of potential complications of continuum modeling for systems with larger misfit.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113785"},"PeriodicalIF":3.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488809","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":"High-Precision identification and classification of alloy fatigue microcracks through deep learning and in-situ SEM","authors":"Zhipeng Chang ,&nbsp;Changhao Wang ,&nbsp;Qianwei Wang ,&nbsp;Xiaopeng Cheng ,&nbsp;Chao Wang ,&nbsp;Xingping Liu ,&nbsp;Bing Wang ,&nbsp;Yuefei Zhang ,&nbsp;Ruzhi Wang","doi":"10.1016/j.commatsci.2025.113795","DOIUrl":"10.1016/j.commatsci.2025.113795","url":null,"abstract":"<div><div>The formation and propagation of microcracks are critical stages leading to fatigue failure. Traditional methods for microcrack analysis essentially rely on manual detection, which poses challenges in accuracy and efficiency. The present investigation deals with a novel and highly effective strategy for detecting, classifying, and analyzing fatigue microcracks in Ti-6Al-4 V (TC4) titanium alloy with artificial intelligence (AI) in the context of deep learning. By integrating <em>in-situ</em> scanning electron microscope (SEM) images and convolutional neural network (CNN) algorithm, we propose a PGI-CrackNet model that is able to detect microcracks of length around 15 μm and thereby effectively outperform the detection capabilities of traditional models. Based on fracture mechanics models, the proposed model is capable of automatically identifying the main stages (i.e., initial crack, type I crack, type II crack, and break) in the formation of microcracks. Simultaneously, the proposed model bridges the gap between the AI-based image analysis and the physical crack propagation models, enabling the extraction of key information such as microcrack length and width, and further supporting the analysis of fatigue crack growth rates associated with various microcrack stages. The model could discover that in the initiation stage, the crack of TC4 titanium alloy grows at a fairly slow rate (∼3.6 μm/cycle) and occupies most of the crack life cycle. After the initiation stage, the crack first propagates as the type I cracks with a significantly faster crack growth rate (∼50 μm/cycle). Then, the type II crack occurs with a substantially reduced growth rate (∼25 μm/cycle). In the final stage, as the microcrack reaches a critical size, the growth rate increases sharply, leading to break. In summary, this improved PGI-CrackNet-based model enables more accurate tracking of crack growth over the fatigue life of materials and better classification of crack types based on their propagation mechanisms, making it highly suitable for early warning applications of material failure.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113795"},"PeriodicalIF":3.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480697","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":"Microscale deformation, residual stress and fracture behavior of additively manufactured alpha-Ti: Combined crystal plasticity and phase-field damage modeling","authors":"Yingying Wang ,&nbsp;Nicolò Grilli ,&nbsp;Michael Salvini ,&nbsp;Yao Yao","doi":"10.1016/j.commatsci.2025.113767","DOIUrl":"10.1016/j.commatsci.2025.113767","url":null,"abstract":"<div><div>Additively manufactured (AM) titanium is a promising material for aerospace, marine engineering and medical equipment because of the flexibility in the manufactured shape and because of their light weight, high strength and corrosion resistance. Plastic deformation and fracture at the microscale have not been widely investigated because of the complexity of carrying out in-situ experiments. In this study, a combined crystal plasticity and phase-field fracture constitutive model for hexagonal close-packed crystal structures is developed, which can simulate the fracture behavior of the alpha phase of AM titanium alloys. An anisotropic thermal expansion model is established to capture the residual stress during the cooling process and its effect on subsequent plastic deformation and fracture. The simulated strain to failure is calibrated with experimental data. The model exhibits strong anisotropy, which agrees with experimental results. Based on the developed model, the influence of microstructural features on the fracture behavior of alpha-Ti is systematically investigated. The findings reveal that grain shape and orientation significantly affect the strain to failure and crack propagation paths of alpha-Ti. Specifically, <span><math><mrow><mo>{</mo><mn>10</mn><mover><mrow><mn>1</mn></mrow><mo>¯</mo></mover><mn>0</mn><mo>}</mo></mrow></math></span> turns out to be responsible for strain localization, and fracture nucleation and propagation The presence of a substantial amount of elongated columnar grains in AM materials is identified as a major factor contributing to anisotropy. The strain failure appears to be higher when load is applied along the maximum principal axis of the grains. The introduction of residual stress increases the final strain to failure in the model; this is interpreted as an acculation of compressive stress in elongated grains with the c axis approximately perpendicular to the load. Furthermore, by altering the local stress distribution, residual stress influences the crack propagation paths. This work provides useful insights into the crack initiation and propagation mechanisms of AM alpha-Ti. The simulation results can also provide guidance for process design such as adjusting scan direction and speed to optimize the microstructural characteristics and, consequently, improve the macroscopic mechanical properties of the material.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113767"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation for structural and electronic properties of Mn-doped perovskite at different doping concentrations","authors":"Baiqian Chen ,&nbsp;Chenyu Jiang ,&nbsp;Yunpeng Wang ,&nbsp;Luchao Du","doi":"10.1016/j.commatsci.2025.113782","DOIUrl":"10.1016/j.commatsci.2025.113782","url":null,"abstract":"<div><div>In the past decade, all-inorganic lead halide perovskites have emerged as a prominent material in optoelectronic field, garnering extensive attention and research. Due to their excellent tunability, many researchers have attempted to enhance the optoelectronic and stability properties of perovskite materials through doping methods. Our study investigates the structural and electronic properties of Mn-doped CsPbBr<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, focusing on the systematic variation introduced by different doping concentrations. The results indicate that as Mn doping content increases, the volume of doped structure tends to decrease gradually, meanwhile the crystals become unstable by degrees. We also discovered that the introduction of Mn has a spin-polarized effect on electronic structure, which introduces new band edge and varies the band gap values. In order to clearly describe the mechanism of Mn doping in CsPbBr<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, a model has been proposed for revealing how Mn doping alters the electronic structure by an indirect way, aiming to provide valuable information for the photoelectric tunability of perovskite materials.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113782"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480699","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":"2D-diffractogram analysis: Kinematic-diffraction simulator for neural-network training-data generation","authors":"Redad Mehdi ,&nbsp;Rounak Chawla ,&nbsp;Erika I. Barcelos ,&nbsp;Matthew A. Willard ,&nbsp;Roger H. French ,&nbsp;Frank Ernst","doi":"10.1016/j.commatsci.2025.113777","DOIUrl":"10.1016/j.commatsci.2025.113777","url":null,"abstract":"<div><div>To exploit the information contained in 2D X-ray diffractograms fully, quantitatively, automatically, and with high throughput, e.g. for analyzing video sequences from in-situ experiments, we can train deep-learning NNs (neural networks) with simulated diffractograms. Realistic models of materials microstructures require “ground truth” training datasets of high cardinality. To produce these, we developed a “kinematic-diffraction simulator,” implemented in the Wolfram Language and executed within a high-performance computing environment. The simulator can rapidly generate Fraunhofer diffractograms for diverse crystal- and microstructure models over a significant multi-dimensional space of parameters. We conclude that simulated diffractograms can enable suitable training of deep-learning NNs – in spite of not including some “real-world” features that occur in experimental diffractograms – and that high-performance computing achieves training data generation rates that support modeling of microstructures with a realistically large number of parameters.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113777"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474734","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":"The yield volume fraction approach to the description of the stress–strain curve of a nickel-base superalloy","authors":"Jingwei Chen ,&nbsp;Alexander M. Korsunsky","doi":"10.1016/j.commatsci.2025.113796","DOIUrl":"10.1016/j.commatsci.2025.113796","url":null,"abstract":"<div><div>The stress–strain curves of most metallic alloys are often described using the relatively simple Ramberg-Osgood relationship. Whilst this description captures the overall stress–strain curve under monotonic tensile loading with reasonable overall accuracy, it often presents significant errors in the immediate post-yield region where the interplay between the elastic and plastic strains is particularly significant. This study proposes and develops a new approach to the description of the tensile stress–strain curve based on the Yield Volume Fraction (YVF) function. The YVF description provides an excellent match to experimental stress–strain curves based on a physically meaningful parameter that corresponds to the cumulative volume fraction of the polycrystal that undergoes yielding during monotonic deformation. The statistical nature of the polycrystal yield phenomenon is highlighted by the fact that the YVF model achieves good agreement with observations when the lognormal and extreme value distributions are employed to express the cumulative density function for the total yield volume fraction, and the probability density function for the incremental yield volume fraction, respectively. This proposed approach is compared with crystal plasticity finite element (CPFE) simulations and different constitutive equations, along with experimental observations. The results highlight the potential of more extensive use of statistical methods in the description of material deformation response for improved design.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113796"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480698","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}