The pressure dependence of magnetic moment in CeMg and PrMg has been studied through ab initio 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 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 18 GPa indicative of pressure induced moment instability caused by an increase of -conduction electron hybridization leading to delocalization of the 4 electrons. In contrast, the magnetic moment of Pr in PrMg3 does not show appreciable change with pressure, indicating strongly localized nature of the 4 electrons.
{"title":"4f Electron Localization–Delocalization studies in CeMg3 and PrMg3 alloys under Pressure","authors":"Kabita Rout , S.K. Mohanta , S.R. Khandual , P.K. Swain , 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 CeMg<sub>3</sub> 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 PrMg<sub>3</sub> 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}
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.
{"title":"DFT and AIMD studies on the conversion and decomposition of Li2S2 to Li2S on 2D-FeS2","authors":"Fen-Ning Zhao , Hong-Tao Xue , Yin-Peng Dong , 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 Li<sub>2</sub>S on 2D-FeS<sub>2</sub> were calculated using the first-principles calculation method. The activation energies for the multistep reduction of S<sub>8</sub> to Li<sub>2</sub>S<sub>4</sub> processes were downhill, indicating that the reaction is relatively easy, except for the conversion of Li<sub>2</sub>S<sub>2</sub> to Li<sub>2</sub>S (Li<sub>2</sub>S<sub>2</sub>RR). Moreover, LiS is likely an intermediate for Li<sub>2</sub>S<sub>2</sub>RR 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 Li<sub>2</sub>S to move quickly to the next step of the vulcanization reaction. This study confirms that the 2D-FeS<sub>2</sub> 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}
Pub Date : 2024-11-15DOI: 10.1016/j.commatsci.2024.113534
Ahmad Ostovari Moghaddam , Rahele Fereidonnejad , Mohammad Moaddeli , Dmitry Mikhailov , Andrey S. Vasenko , Evgeny Trofimov
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.
{"title":"Second nearest-neighbor modified embedded-atom method interatomic potentials for the Zr-X (X = Co, Fe, Ni) binary alloys","authors":"Ahmad Ostovari Moghaddam , Rahele Fereidonnejad , Mohammad Moaddeli , Dmitry Mikhailov , Andrey S. Vasenko , 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}
Pub Date : 2024-11-14DOI: 10.1016/j.commatsci.2024.113481
Joshua D. Pribe , Patrick E. Leser , Saikumar R. Yeratapally , Edward H. Glaessgen
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 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.
复杂、随机的微观结构可能会对添加制造金属的机械性能产生重大不确定性。仅使用实验来量化这种不确定性会耗费大量的时间和金钱成本。有限的实验也难以确定加工、微观结构和微观机械行为之间的定量关系。晶体塑性模拟有助于减少对实验的依赖，以预测微观结构不确定性对微观机械相关量（QoIs）的影响。然而，全场晶体塑性模型的评估计算成本很高，因此在使用标准蒙特卡罗（MC）方法进行不确定性传播时不具吸引力。低保真度模型的评估速度可能更快，但通常存在偏差，准确性较低。多模型 MC 方法结合了两个或多个不同保真度的模型，可以更有效地传播不确定性，并提供无偏的 QoI 估计值。在这项工作中，我们采用了多模型 MC 框架，使用基于全场和均质化模型的集合预测晶体塑性 QoI，其微观结构基于添加式制造的镍基超级合金。QoIs 是每个微结构实例中的总屈服强度以及晶粒平均应力和应变量的平均值和最大值。通过为每个模型优化分配样本，在计算成本相同的情况下，与标准 MC 相比，QoIs 的方差最多可减少 ∼ 20 倍。等效地，在目标方差相同的情况下，方差降低可视为计算成本的降低。由此可见，多模型 MC 是有效传播晶体塑性模型不确定性的一种可行方法。
{"title":"Multi-model Monte Carlo estimation for crystal plasticity structure–property simulations of additively manufactured metals","authors":"Joshua D. Pribe , Patrick E. Leser , Saikumar R. Yeratapally , 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}
Pub Date : 2024-11-14DOI: 10.1016/j.commatsci.2024.113458
Ching-Chien Chen, Robert J. Appleton, Kat Nykiel, Saswat Mishra, Shukai Yao, Alejandro Strachan
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.
密度泛函理论（DFT）被广泛用于研究材料在高压下的行为，以补充具有挑战性且通常成本高昂的实验。虽然对 DFT 的准确性以及各种近似和修正对环境条件下材料特性的影响进行了广泛研究，但很少有研究对高压下的准确性进行量化。我们重点研究了所选材料的状态方程（EOS）的预测精度（最高可达 100 GPa）以及压力诱导相变的描述。我们描述了交换相关函数、伪势、色散和 Hubbard U 校正的影响，并发现在环境条件下获得的经验并不总能转化为高压机制。我们发现，与广义梯度近似（GGA）的 Perdew-Burke-Erzerhof 版本、局部密度近似（LDA）和 Heyd-Scuseria-Ernzerhof （HSE）混合函数相比，广义梯度近似（GGA）的 Perdew-Burke-Erzerhof 固体版本在 EOS 和转换压力方面都具有最佳性能。添加称为 D2 和 D3 的色散修正并不能改善结果。有趣的是，局部密度近似的效果非常好。我们还发现，在强相关材料系统中，Hubbard-U 修正对转化压力有显著影响，这表明必须谨慎选择 U 参数。这项研究的一个重要副产品是 nanoHUB 上的高压模拟数据库 FAIR 资源库。
{"title":"How accurate is density functional theory at high pressures?","authors":"Ching-Chien Chen, Robert J. Appleton, Kat Nykiel, Saswat Mishra, Shukai Yao, 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}
Pub Date : 2024-11-13DOI: 10.1016/j.commatsci.2024.113493
Christoph Dösinger , Thomas Hammerschmidt , Oleg Peil , Daniel Scheiber , Lorenz Romaner
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 -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.
合金元素在晶界（GB）的偏析对材料的机械和功能特性有重大影响。这一过程受偏析能的控制，而偏析能可通过非线性方法精确计算。在过去几年中，为了降低计算成本，模拟计算已经与机器学习（ML）方法相结合。在这里，我们展示了如何将电子结构信息纳入 ML。为了获得电子结构，我们使用了两种方法：(i) 密度泛函理论（DFT）；(ii) 紧结合（TB）哈密顿的递归解。利用推导出的描述符，我们对来自 15 个重合位点晶格 GB（Σ 值最高可达 43）的非原位偏析数据训练了一个线性模型和一个高斯过程，并利用交叉验证得分对模型进行了比较。结果发现，TB 和 DFT 衍生的描述符都明显优于以前用于 ML 离析能的普通结构特征。此外，TB 描述符几乎达到了与 DFT 描述符相同的精度，但其计算量却大大减少。我们在 bcc-W 矩阵中将 Ta 和 Re 分离成 GBs 的过程中测试了我们的方法，这些材料与聚变能研究息息相关。
{"title":"Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies","authors":"Christoph Dösinger , Thomas Hammerschmidt , Oleg Peil , Daniel Scheiber , 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}
Pub Date : 2024-11-12DOI: 10.1016/j.commatsci.2024.113509
Sandip Guin , Albert Linda , Yu-Chieh Lo , Somanth Bhowmick , Rajdip Mukherjee
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 -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 () and time () demonstrates a linear correlation. With Cr segregation at GB, the vs. 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.
{"title":"Effect of Cr segregation on grain growth in nanocrystalline α-Fe alloy: A multiscale modeling approach","authors":"Sandip Guin , Albert Linda , Yu-Chieh Lo , Somanth Bhowmick , 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}
Pub Date : 2024-11-12DOI: 10.1016/j.commatsci.2024.113487
D.K. Das, B. Kumar
Background
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.
Review Factor: 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.
Conclusions
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.
背景由于铅的独特性质和在多个领域的广泛应用，二维单原子层铅烯成为全球科学家和研究人员关注的焦点：Plumbene 可应用于柔性电子领域、氢吸附、变性半导体、拓扑绝缘体等。本文讨论了铅笔烯的原子结构、迄今为止对铅笔烯所做的研究以及铅笔烯在社会中的未来应用范围。通过 Ab Initio 计算研究了其原子结构和电子特性。这些计算采用了牛顿第二运动定律和经典力学方法。我们可以看到，在相同参数（如应变值、加载条件、平衡和非平衡分子动力学）下的结果会受到所采用理论的影响。
{"title":"Plumbene a promising material for future technology: A review","authors":"D.K. Das, 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}
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 and Si, and on n-type doping of Si and Se. We found that the most favorable point defects formed by C, O, and Si are C, OO, Si, CSi, and OSi, which have formation energies of less than . 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 AsB 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.
立方砷化硼（BAs）具有优异的热导率和极性迁移率，是一种很有前途的先进电子材料。然而，有效控制砷化硼中的 p 型和 n 型掺杂是一项重大挑战，这主要是由于缺陷的影响。在本研究中，我们采用密度泛函理论（DFT）探讨了常见点缺陷和杂质对 BeB 和 SiAs 的 p 型掺杂以及 SiB 和 SeAs 的 n 型掺杂的影响。我们发现，由 C、O 和 Si 形成的最有利的点缺陷是 CAs、OBOAs、SiAs、CAsSiB 和 OBSiAs，它们的形成能量小于 1.5eV。O 杂质对 p 型和 n 型掺杂都有不利影响，而 C 和 Si 杂质则对 n 型掺杂有害，因此 n 型掺杂是一个潜在的挑战。有趣的是，反位缺陷对 AsBBAs 有利于 p 型和 n 型掺杂。本研究提出的掺杂限制分析有可能为基于 BAs 的电子学领域的战略发展铺平道路。
{"title":"Impacts of point defects on shallow doping in cubic boron arsenide: A first principles study","authors":"Shuxiang Zhou , Zilong Hua , Kaustubh K. Bawane , Hao Zhou , 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}
Pub Date : 2024-11-11DOI: 10.1016/j.commatsci.2024.113507
Xuefei Wang , Chunyang Luo , Di Jiang , Haojie Wang , Zhaodong Wang
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.
{"title":"Improved design method for gas carburizing process through data-driven and physical information","authors":"Xuefei Wang , Chunyang Luo , Di Jiang , Haojie Wang , 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}
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