Computational Materials Science最新文献

{"title":"4f Electron Localization–Delocalization studies in CeMg3 and PrMg3 alloys under Pressure","authors":"Kabita Rout ,&nbsp;S.K. Mohanta ,&nbsp;S.R. Khandual ,&nbsp;P.K. Swain ,&nbsp;S.N. Mishra","doi":"10.1016/j.commatsci.2024.113514","DOIUrl":"10.1016/j.commatsci.2024.113514","url":null,"abstract":"<div><div>The pressure dependence of magnetic moment in CeMg<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> and PrMg<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> has been studied through <em>ab initio</em> electronic structure calculations based on density functional theory (DFT). Positive as well as negative pressure conditions were simulated by different degrees of unit cell compression or expansion and a fit of the total energy to the Birch–Murnaghan equation of state. At ambient and negative pressures, the calculated magnetic moments for both the compounds reveal localized behaviour of 4<span><math><mi>f</mi></math></span> electrons. For increasing positive pressures, the magnetic moment of Ce in CeMg₃ has been observed to diminish smoothly, becoming zero at a critical pressure of P<span><math><mrow><msub><mrow></mrow><mrow><mi>C</mi></mrow></msub><mo>∼</mo></mrow></math></span> 18 GPa indicative of pressure induced moment instability caused by an increase of <span><math><mi>f</mi></math></span>-conduction electron hybridization leading to delocalization of the 4<span><math><mi>f</mi></math></span> electrons. In contrast, the magnetic moment of Pr in PrMg₃ does not show appreciable change with pressure, indicating strongly localized nature of the 4<span><math><mi>f</mi></math></span> electrons.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113514"},"PeriodicalIF":3.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656993","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":"DFT and AIMD studies on the conversion and decomposition of Li2S2 to Li2S on 2D-FeS2","authors":"Fen-Ning Zhao ,&nbsp;Hong-Tao Xue ,&nbsp;Yin-Peng Dong ,&nbsp;Fu-Ling Tang","doi":"10.1016/j.commatsci.2024.113531","DOIUrl":"10.1016/j.commatsci.2024.113531","url":null,"abstract":"<div><div>Anchoring polysulfides to prevent their shuttling and dissolution into the electrolyte of Li-S batteries has been extensively studied. Whereas, the sulfur reduction reaction kinetics and the conversion process of lithium polysulfides are still unclear. In this study, the transformation of LiPSs and the decomposition of Li₂S on 2D-FeS₂ were calculated using the first-principles calculation method. The activation energies for the multistep reduction of S₈ to Li₂S₄ processes were downhill, indicating that the reaction is relatively easy, except for the conversion of Li₂S₂ to Li₂S (Li₂S₂RR). Moreover, LiS is likely an intermediate for Li₂S₂RR conversion, with optimal adsorption strength and low activation energy using the computational hydrogen electrode (CHE) approach. The dynamic results indicate that the lower decomposition barriers enable the deposited Li₂S to move quickly to the next step of the vulcanization reaction. This study confirms that the 2D-FeS₂ cathode material significantly contributes to the electrocatalytic reaction and shows promise in addressing the challenges of Li-S batteries by reducing the activation energy during the conversion process in the future.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113531"},"PeriodicalIF":3.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656994","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":"Second nearest-neighbor modified embedded-atom method interatomic potentials for the Zr-X (X = Co, Fe, Ni) binary alloys","authors":"Ahmad Ostovari Moghaddam ,&nbsp;Rahele Fereidonnejad ,&nbsp;Mohammad Moaddeli ,&nbsp;Dmitry Mikhailov ,&nbsp;Andrey S. Vasenko ,&nbsp;Evgeny Trofimov","doi":"10.1016/j.commatsci.2024.113534","DOIUrl":"10.1016/j.commatsci.2024.113534","url":null,"abstract":"<div><div>The second nearest-neighbor modified embedded-atom method (2NN-MEAM) interatomic potentials were developed for Zr-X (X = Co, Fe, Ni) binary alloys. The structural, mechanical and thermodynamic properties of various stable and metastable phases in Zr-Co, Zr-Fe and Zr-Ni binary systems were calculated by molecular dynamic (MD) simulation using the developed 2NN-MEAM potentials. The results obtained by MD simulation using the 2NN-MEAM potentials exhibited good consistency with the experimental data or first-principles calculations. The potentials can be utilized to investigate the atomic scale physical metallurgy of Zr-based binary, multinary and high entropy alloys and adjust their composition and microstructure to meet the specific requirements entailed in harsh environments.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113534"},"PeriodicalIF":3.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656996","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":"Multi-model Monte Carlo estimation for crystal plasticity structure–property simulations of additively manufactured metals","authors":"Joshua D. Pribe ,&nbsp;Patrick E. Leser ,&nbsp;Saikumar R. Yeratapally ,&nbsp;Edward H. Glaessgen","doi":"10.1016/j.commatsci.2024.113481","DOIUrl":"10.1016/j.commatsci.2024.113481","url":null,"abstract":"<div><div>Significant uncertainty in the mechanical behavior of additively manufactured metals can arise from complex, stochastic microstructures. Using experiments alone to quantify this uncertainty incurs significant time and monetary costs. Quantitative relationships across processing, microstructure, and micromechanical behavior are also difficult to establish with limited experiments. Crystal plasticity simulations can help to reduce reliance on experiments for predicting the influence of microstructural uncertainty on micromechanical quantities of interest (QoIs). However, full-field crystal plasticity models are computationally expensive to evaluate, making them unattractive for uncertainty propagation with standard Monte Carlo (MC) methods. Lower-fidelity models may be faster to evaluate but are generally biased and less accurate. Multi-model MC methods combine two or more models of varying fidelities to more efficiently propagate uncertainty and provide unbiased QoI estimates. In this work, a multi-model MC framework is applied to predict crystal plasticity QoIs using an ensemble of full-field and homogenization-based models with microstructures based on additively manufactured Ni-base superalloys. The QoIs are the aggregate yield strength and the mean and maximum values of grain-average stress and strain quantities in each microstructure instantiation. By optimally allocating samples to each model, up to <span><math><mrow><mo>∼</mo><mn>20</mn><mo>×</mo></mrow></math></span> variance reduction is achieved for the QoIs relative to standard MC with the same computational cost constraint. Equivalently, the variance reduction can be viewed as a computational cost reduction given the same target variance. Multi-model MC is thereby shown to be a promising approach for efficiently propagating uncertainty with crystal plasticity models.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113481"},"PeriodicalIF":3.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656997","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":"How accurate is density functional theory at high pressures?","authors":"Ching-Chien Chen,&nbsp;Robert J. Appleton,&nbsp;Kat Nykiel,&nbsp;Saswat Mishra,&nbsp;Shukai Yao,&nbsp;Alejandro Strachan","doi":"10.1016/j.commatsci.2024.113458","DOIUrl":"10.1016/j.commatsci.2024.113458","url":null,"abstract":"<div><div>Density functional theory (DFT) is widely used to study the behavior of materials at high pressures, complementing challenging and often costly experiments. While the accuracy of DFT and the effect of various approximations and corrections have been extensively studied for materials properties around ambient conditions, few studies quantified accuracy at high pressures. We focus on the accuracy of predicted equations of state (EOS) of selected materials up to the hundred GPa regime and the description of pressure-induced phase transformations. We characterize the effect of exchange–correlation functionals, pseudopotentials, dispersion and Hubbard U correction and find that lessons-learned at ambient conditions do not always translate into the high-pressure regime. We find that the Perdew-Burke-Erzerhof solid version of the generalized gradient approximation (GGA) yields the best performance in both EOS and transformation pressure compared to Perdew-Burke-Erzerhof version of GGA, local density approximations (LDA), and the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional. Adding dispersion corrections known as D2 and D3 does not improve the results. Interestingly, the local density approximation performed remarkably well. We also find that the Hubbard-U correction as a significant effect on transformation pressures in strongly correlated materials systems, indicating that the U parameter must be chosen carefully. An important by-product of this study is a FAIR repository of high-pressure simulations database on nanoHUB.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113458"},"PeriodicalIF":3.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656995","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":"Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies","authors":"Christoph Dösinger ,&nbsp;Thomas Hammerschmidt ,&nbsp;Oleg Peil ,&nbsp;Daniel Scheiber ,&nbsp;Lorenz Romaner","doi":"10.1016/j.commatsci.2024.113493","DOIUrl":"10.1016/j.commatsci.2024.113493","url":null,"abstract":"<div><div>The segregation of alloying elements to grain-boundaries (GB) has a significant impact on mechanical and functional properties of materials. The process is controlled by the segregation energies, that can accurately be computed using ab-initio methods. Over the last years, ab-initio computations have been combined with machine-learning (ML) approaches for a reduction of computational cost. Here, we show how information from the electronic structure can be incorporated in the ML. To obtain the electronic structure we use two methods, (i) density functional theory (DFT), and (ii) a recursive solution of a tight-binding (TB) Hamiltonian. With the derived descriptors we train a linear model and a Gaussian process on ab-initio segregation data from 15 coincident site lattice GBs with <span><math><mi>Σ</mi></math></span>-values up to 43, where the models are compared using cross-validation scores. Both the TB and DFT-derived descriptors are found to clearly outperform common structure-based features that have been used for ML segregation energies before. Furthermore, TB descriptors almost reach the same accuracy as DFT descriptors although their computational effort is significantly reduced. We test our approach on segregation of Ta and Re to GBs in a bcc-W matrix, which are materials of relevance for fusion-energy research.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113493"},"PeriodicalIF":3.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656992","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":"Effect of Cr segregation on grain growth in nanocrystalline α-Fe alloy: A multiscale modeling approach","authors":"Sandip Guin ,&nbsp;Albert Linda ,&nbsp;Yu-Chieh Lo ,&nbsp;Somanth Bhowmick ,&nbsp;Rajdip Mukherjee","doi":"10.1016/j.commatsci.2024.113509","DOIUrl":"10.1016/j.commatsci.2024.113509","url":null,"abstract":"<div><div>We present a multiscale modeling framework that integrates density functional theory (DFT) with a phase-field model (PFM) to explore the intricate dynamics of grain growth in nanocrystalline <span><math><mi>α</mi></math></span>-Fe single-phase alloy in the presence of chromium (Cr) segregation. Simulated results for equilibrium segregation in stationary grain boundary (GB) agree with the Mclean isotherm, validating our model. Polycrystal simulations featuring nanocrystalline grains at different temperatures reveal that the grain growth kinetics depends on the ratio of Cr diffusivity to intrinsic GB mobility. Without Cr segregation at GB, the relationship between the square of average grain size (<span><math><msup><mrow><mi>d</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) and time (<span><math><mi>t</mi></math></span>) demonstrates a linear correlation. With Cr segregation at GB, the <span><math><msup><mrow><mi>d</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> vs. <span><math><mi>t</mi></math></span> plot initially follows the same linear growth trajectory as observed without segregation up to a threshold grain size, beyond which it deviates with a decreasing slope. The threshold grain size decreases with increasing temperature from 700K to 900K. Notably, at 1000K, grain growth without and with Cr segregation both follow a linear trajectory, the latter having a smaller slope from the beginning. We develop an analytical formulation based on Cahn’s solute drag theory to predict grain growth in the presence of solute segregation at GB and use it to validate our simulation results.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113509"},"PeriodicalIF":3.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656991","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":"Plumbene a promising material for future technology: A review","authors":"D.K. Das,&nbsp;B. Kumar","doi":"10.1016/j.commatsci.2024.113487","DOIUrl":"10.1016/j.commatsci.2024.113487","url":null,"abstract":"<div><h3>Background</h3><div>Due to enormous unique properties and wide applications in several sectors, plumbene, the two dimensional single atomic layer of lead is the centre of attraction for scientists and researchers around the globe.</div><div><strong>Review Factor:</strong> Plumbene finds its applications in the flexible electronics field, in hydrogen adsorption, as degenerating semiconductor, topological insulator etc. In this paper, we discuss the atomic structure of plumbene, research done on plumbene till now which necessitates future scope of plumbene in society.</div></div><div><h3>Conclusions</h3><div>Modeling and simulation are the most techniques adopted to evaluate mechanical and thermal properties of plumbene. Its atomic structure and electronic properties are studied by Ab Initio calculations. Newton’s second law of motion and classical mechanics methods are adopted for these calculations. We can see that the results under the same parameter such as strain value, loading conditions, equilibrium and non-equilibrium molecular dynamics are affected by the implemented theories.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113487"},"PeriodicalIF":3.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657111","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":"Impacts of point defects on shallow doping in cubic boron arsenide: A first principles study","authors":"Shuxiang Zhou ,&nbsp;Zilong Hua ,&nbsp;Kaustubh K. Bawane ,&nbsp;Hao Zhou ,&nbsp;Tianli Feng","doi":"10.1016/j.commatsci.2024.113483","DOIUrl":"10.1016/j.commatsci.2024.113483","url":null,"abstract":"<div><div>Cubic boron arsenide (BAs) stands out as a promising material for advanced electronics, thanks to its exceptional thermal conductivity and ambipolar mobility. However, effective control of p- and n-type doping in BAs poses a significant challenge, mostly as a result of the influence of defects. In the present study, we employed density functional theory (DFT) to explore the impacts of the common point defects and impurities on p-type doping of Be<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span> and Si<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>, and on n-type doping of Si<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span> and Se<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>. We found that the most favorable point defects formed by C, O, and Si are C<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>, O<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>, Si<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>, C<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>Si<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span>, and O<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span>Si<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span>, which have formation energies of less than <span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mspace></mspace><mi>eV</mi></mrow></math></span>. While the O impurity detrimentally affects both p- and n-type dopings, C and Si impurities are harmful for n-type dopings, making n-type doping a potential challenge. Interestingly, the antisite defect pair As<span><math><msub><mrow></mrow><mrow><mtext>B</mtext></mrow></msub></math></span>B<span><math><msub><mrow></mrow><mrow><mtext>As</mtext></mrow></msub></math></span> benefits both p- and n-type doping. The doping limitation analysis presented in this study can potentially pave the way for strategic development in the area of BAs-based electronics.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113483"},"PeriodicalIF":3.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656990","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":"Improved design method for gas carburizing process through data-driven and physical information","authors":"Xuefei Wang ,&nbsp;Chunyang Luo ,&nbsp;Di Jiang ,&nbsp;Haojie Wang ,&nbsp;Zhaodong Wang","doi":"10.1016/j.commatsci.2024.113507","DOIUrl":"10.1016/j.commatsci.2024.113507","url":null,"abstract":"<div><div>Data- or physics-driven computational simulation algorithms have gained widespread attention in the field of scientific computing. However, most existing methods rely solely on either data or physical information to solve problems, making them susceptible to the complexities of physical processes or issues such as data loss and distortion. In this paper, we propose a dual-driven simulation method that combines data and physical information to improve the accuracy and stability of carburizing heat treatment simulations, specifically addressing the data loss problem faced by purely data-driven models. By embedding Fick’s second law into the deep learning framework, we created a Physics-Informed Neural Network (PINN) to simulate the transfer and diffusion of carbon elements in the carburizing process. This method breaks the neural network’s dependence on data. Based on this, we developed an efficient gas carburizing process design method and validated its accuracy and efficiency on typical carburizing steel, with a deviation of only 0.008% from the target carbon concentration. In terms of neural network solver design, we optimized and discussed the network’s hyper-parameters, finding that a network design with three hidden layers offers the best accuracy for this type of problem without imposing a heavy computational burden. Compared to classical numerical solvers, this method increases computational speed by several orders of magnitude.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"247 ","pages":"Article 113507"},"PeriodicalIF":3.1,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656939","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}