Computational Materials Science最新文献

{"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}

{"title":"High performance additive manufacturing phase field simulation: Fortran Do Concurrent vs OpenMP","authors":"Shahid Maqbool,&nbsp;Byeong-Joo Lee","doi":"10.1016/j.commatsci.2025.113788","DOIUrl":"10.1016/j.commatsci.2025.113788","url":null,"abstract":"<div><div>Standard language parallelism is an alternate way to achieve the parallel performance of the code without using external application processing interface (API). In this work, we present the Fortran Do Concurrent standard language parallel feature for additive manufacturing. We developed an open-source AMSimulator application and have implemented OpenMP and Fortran Do Concurrent in the phase field simulation. Performance has been measured across various platforms like Windows 10 and Linux and open-source compilers with Intel and NVIDIA. We found that using standard language parallel features, the same performance can be achieved without the need of external API. This high-performance approach is useful for code development and portability across various platforms.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113788"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474735","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":"An automatic scientific data collection framework for materials science","authors":"Ziyi Chen ,&nbsp;Yang Yuan ,&nbsp;Sihan Liang ,&nbsp;Meng Wan ,&nbsp;Kai Li ,&nbsp;Weiqi Zhou ,&nbsp;Yangang Wang ,&nbsp;Zongguo Wang","doi":"10.1016/j.commatsci.2025.113772","DOIUrl":"10.1016/j.commatsci.2025.113772","url":null,"abstract":"<div><div>With the rapid development of information technology, there has been an exponential increase in material data. However, challenges such as inconsistencies in data formats and non-standardized storage methods have emerged as primary obstacles for researchers seeking to harness materials science data effectively. To fully exploit material data from diverse sources and achieve the efficient fusion of historical data, this paper introduces a database application framework designed for the automatic collection and analysis of multi-source heterogeneous material data, and two first principles calculations datasets are established. Standardized methods used in this work enable the automatic extraction, storage and analysis of both discrete and database data while also offering an interface for data-driven scientific research. Moreover, this framework used for dataset construction can be deployed in both cloud-based virtual environments and local servers, providing flexibility that not only facilitates data sharing but also ensures data privacy and customized control. The datasets and framework developed in this work offer a robust data foundation and potent tool for researchers engaged in data-driven research.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113772"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474737","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":"Fully automated high-throughput computer-based catalytic material screening framework and its application on the new-generation Tianhe supercomputer","authors":"Can Leng ,&nbsp;Xuguang Chen ,&nbsp;Jie Liu ,&nbsp;Chunye Gong ,&nbsp;Bo Yang ,&nbsp;Zhuo Tang ,&nbsp;Wangdong Yang ,&nbsp;Wei-Qing Huang ,&nbsp;Yi-Ge Zhou ,&nbsp;Mengxia Mo ,&nbsp;Kenli Li ,&nbsp;Keqin Li","doi":"10.1016/j.commatsci.2025.113775","DOIUrl":"10.1016/j.commatsci.2025.113775","url":null,"abstract":"<div><div>The integration of high-performance computing with machine learning (ML) has established a transformative scientific paradigm that significantly enhances the efficiency of material discovery, particularly in the search for catalysts in alternative energy research. However, significant challenges remain in the utilization of available computational resources to accelerate the screening of catalyst materials. In this study, we implement a high-throughput framework on the new-generation Tianhe supercomputer, featuring the development of a Ping-Fault Recovery algorithm, single-task optimization for Density Functional Theory (DFT) to maximize efficiency, and enhanced task scheduling using a two-level scheduling strategy to ensure efficient utilization of the abundant computational resources of the supercomputer. This framework facilitates the identification of 2,028 candidate surfaces across 868 intermetallics from 2,713,897 unique adsorption sites, achieving a screening speed 193 times faster than traditional methods. Alloys composed of Mo, Nb, and V are used as case studies to provide a detailed elucidation of the process of identifying the most effective catalytic surfaces. The framework achieved the best single-day candidate hit performance on 18,106 nodes, completing in one day what previously took a year. This supercomputer-based framework optimizes the use of computational resources, driving innovation in catalyst material discovery.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113775"},"PeriodicalIF":3.1,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471482","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 novel constrained sampling method for efficient exploration in materials and chemical mixture design","authors":"Christina Schenk,&nbsp;Maciej Haranczyk","doi":"10.1016/j.commatsci.2025.113780","DOIUrl":"10.1016/j.commatsci.2025.113780","url":null,"abstract":"<div><div>Efficient exploration of multicomponent material composition spaces is often limited by time and financial constraints, particularly when mixture and synthesis constraints exist. Traditional methods like Latin hypercube sampling (LHS) struggle with constrained problems especially in high dimensions, while emerging approaches like Bayesian optimization (BO) face challenges in early-stage exploration. This article introduces ConstrAined Sequential laTin hypeRcube sampling methOd (CASTRO), an open-source tool designed to address these challenges. CASTRO is optimized for uniform sampling in constrained small- to moderate-dimensional spaces, with scalability to higher dimensions through future adaptations. CASTRO uses a divide-and-conquer strategy to decompose problems into parallel subproblems, improving efficiency and scalability. It effectively handles equality-mixture constraints, ensuring comprehensive design space coverage and leveraging LHS and LHS with multidimensional uniformity (LHSMDU). It also integrates prior experimental knowledge, making it well-suited for efficient exploration within limited budgets. Validation through two material design case studies, a four-dimensional problem with near-uniform distributions and a nine-dimensional problem with additional synthesis constraints, demonstrates CASTRO’s effectiveness in exploring constrained design spaces for materials science, pharmaceuticals and chemicals. The software and case studies are available on GitHub.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"252 ","pages":"Article 113780"},"PeriodicalIF":3.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463870","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}