Pub Date : 2024-05-09DOI: 10.1088/1361-651x/ad4407
Rahul Ghosh, Bhavana Sahu, Arjun Dey, Hari Krishna Thota, Karabi Das
Nowadays, anodic coating on additively manufactured (AM) or 3D printed Al–10Si–Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, artificial neural network (ANN) and power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al–10Si–Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al–10Si–Mg alloy was considered for modeling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al–10Si–Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al–10Si–Mg alloy.
如今，阳极涂层添加剂制造（AM）或三维打印的 Al-10Si-Mg 合金被用于航天器中的各种部件，如天线馈线、波导、结构支架、准直器、热辐射器等。本研究根据纳米压痕实验数据开发了基于人工神经网络（ANN）和幂律的模型，用于预测阳极氧化 AM Al-10Si-Mg 在任何所需载荷下的弹性模量和硬度。建模时考虑了对 AM Al-10Si-Mg 合金阳极氧化涂层的平面和横截面进行的纳米压痕实验数据。除纳米力学性能外，还使用 Python 软件根据 ANN 和纳米压痕幂律模型预测了载荷和位移曲线。据观察，50 毫牛顿纳米压痕实验数据的 ANN 模型可以准确预测 50 毫牛顿以下任何所需载荷的加载模式。根据 ANN 和卸载曲线的幂律模型计算出的阳极氧化 AM Al-10Si-Mg 的弹性模量和硬度值也与其他地方报道的通过 Weibull 分布分析获得的值相当。推导出的模型还用于预测 25 和 35 mN 条件下的纳米力学性能，因为没有这方面的实验数据。阳极涂层平面部分的计算硬度在 25 和 35 毫牛顿时分别为 3.99 和 4.02 GPa。阳极涂层横截面的计算硬度在 25 和 35 mN 条件下分别为 7.16 和 6.61 GPa。因此，纳米压痕的 ANN 和幂律模型可以通过进行最少的实验来预测不同载荷下的弹性模量和硬度。利用 ANN 预测纳米力学性能的新方法确定了 AM Al-10Si-Mg 合金阳极氧化涂层硬度和模量的现实和设计特定数据。
{"title":"Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy","authors":"Rahul Ghosh, Bhavana Sahu, Arjun Dey, Hari Krishna Thota, Karabi Das","doi":"10.1088/1361-651x/ad4407","DOIUrl":"https://doi.org/10.1088/1361-651x/ad4407","url":null,"abstract":"Nowadays, anodic coating on additively manufactured (AM) or 3D printed Al–10Si–Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, artificial neural network (ANN) and power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al–10Si–Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al–10Si–Mg alloy was considered for modeling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al–10Si–Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al–10Si–Mg alloy.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"156 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-05-09DOI: 10.1088/1361-651x/ad4408
George Petsos
We examine the influence of grains size on the stability of polycrystalline coherent binary solid solutions. By assuming that the grains are isotropic, we find that the tendency for instability decreases as the radius of the grains decrease. We also find that a temperature-dependent critical grain radius exists below which the tendency for instability vanishes and the grains are stable, with respect to infinitesimal composition fluctuations, for any initial composition. We find that the critical grain radius decreases monotonically as the temperature decrease. If the radius of the grains is smaller than the minimum critical grain radius the grains are stable for any temperature and initial composition.
{"title":"Size effects on spinodal decomposition","authors":"George Petsos","doi":"10.1088/1361-651x/ad4408","DOIUrl":"https://doi.org/10.1088/1361-651x/ad4408","url":null,"abstract":"We examine the influence of grains size on the stability of polycrystalline coherent binary solid solutions. By assuming that the grains are isotropic, we find that the tendency for instability decreases as the radius of the grains decrease. We also find that a temperature-dependent critical grain radius exists below which the tendency for instability vanishes and the grains are stable, with respect to infinitesimal composition fluctuations, for any initial composition. We find that the critical grain radius decreases monotonically as the temperature decrease. If the radius of the grains is smaller than the minimum critical grain radius the grains are stable for any temperature and initial composition.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"11 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This reply is addressed to comments on our paper entitled ‘Composition-based Aluminum Alloy Selection Using an Artificial Neural Network.’ There are six main comments, and we addressed the comments carefully. This machine learning (ML) modeling is only part of the development of a broader material selection (or material screening) system. Consideration of other material properties can certainly be included through the integration of ML systems.
本答复是针对我们题为《使用人工神经网络进行基于成分的铝合金选择》的论文所提出的意见。主要有六条意见，我们已认真处理了这些意见。这种机器学习（ML）建模只是开发更广泛的材料选择（或材料筛选）系统的一部分。通过集成 ML 系统，当然还可以考虑其他材料特性。
{"title":"Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’","authors":"Jaka Fajar Fatriansyah, Raihan Kenji Rizqillah, Iping Suhariadi, Andreas Federico, Ade Kurniawan","doi":"10.1088/1361-651x/ad4574","DOIUrl":"https://doi.org/10.1088/1361-651x/ad4574","url":null,"abstract":"This reply is addressed to comments on our paper entitled ‘Composition-based Aluminum Alloy Selection Using an Artificial Neural Network.’ There are six main comments, and we addressed the comments carefully. This machine learning (ML) modeling is only part of the development of a broader material selection (or material screening) system. Consideration of other material properties can certainly be included through the integration of ML systems.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"42 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140931107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-05-08DOI: 10.1088/1361-651x/ad4573
Russell Wanhill
This article comments on the article ‘Composition-based aluminum alloy selection using an artificial neural network previously published in this journal. It is shown that the input information of the modelling is much too limited and the selection procedure is simplistic and not applicable or relevant to the actual selection procedures for aerospace aluminum alloys. The modelling has been done without sufficient engineering knowledge (almost none) about the properties, selection criteria, alloy compositions and processing of aerospace structural aluminum alloys.
{"title":"Comment on ‘Composition-based aluminum alloy selection using an artificial neural network’","authors":"Russell Wanhill","doi":"10.1088/1361-651x/ad4573","DOIUrl":"https://doi.org/10.1088/1361-651x/ad4573","url":null,"abstract":"This article comments on the article ‘Composition-based aluminum alloy selection using an artificial neural network previously published in this journal. It is shown that the input information of the modelling is much too limited and the selection procedure is simplistic and not applicable or relevant to the actual selection procedures for aerospace aluminum alloys. The modelling has been done without sufficient engineering knowledge (almost none) about the properties, selection criteria, alloy compositions and processing of aerospace structural aluminum alloys.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"3 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-05-08DOI: 10.1088/1361-651x/ad42bb
Lucas Benoit-Maréchal, Marco Salvalaglio
The Swift–Hohenberg (SH) and phase-field crystal (PFC) models are minimal yet powerful approaches for studying phenomena such as pattern formation, collective order, and defects via smooth order parameters. They are based on a free-energy functional that inherently includes elasticity effects. This study addresses how gradient elasticity (GE), a theory that accounts for elasticity effects at microscopic scales by introducing additional characteristic lengths, is incorporated into SH and PFC models. After presenting the fundamentals of these theories and models, we first calculate the characteristic lengths for various lattice symmetries in an approximated setting. We then discuss numerical simulations of stress fields at dislocations and comparisons with analytic solutions within first and second strain-gradient elasticity. Effective GE characteristic lengths for the elastic fields induced by dislocations are found to depend on the free-energy parameters in the same manner as the phase correlation length, thus unveiling how they change with the quenching depth. The findings presented in this study enable a thorough discussion and analysis of small-scale elasticity effects in pattern formation and crystalline systems using SH and PFC models and, importantly, complete the elasticity analysis therein. Additionally, we provide a microscopic foundation for GE in the context of order-disorder phase transitions.
斯威夫特-霍恩伯格（SH）模型和相场晶体（PFC）模型是研究图案形成、集体有序和通过平滑有序参数产生缺陷等现象的最简便而又强大的方法。它们以自由能函数为基础，其中固有地包含了弹性效应。梯度弹性（GE）理论通过引入额外的特征长度来解释微观尺度上的弹性效应，本研究探讨了如何将梯度弹性纳入 SH 和 PFC 模型。在介绍了这些理论和模型的基本原理后，我们首先计算了各种晶格对称性的近似特征长度。然后，我们讨论了位错应力场的数值模拟以及与第一和第二应变梯度弹性分析解的比较。我们发现，位错诱导的弹性场的有效 GE 特性长度与相相关长度一样取决于自由能参数，从而揭示了它们如何随淬火深度而变化。本研究中的发现使我们能够利用 SH 和 PFC 模型对图案形成和晶体系统中的小尺度弹性效应进行全面的讨论和分析，重要的是完成了其中的弹性分析。此外，我们还为有序-无序相变背景下的 GE 提供了微观基础。
{"title":"Gradient elasticity in Swift–Hohenberg and phase-field crystal models","authors":"Lucas Benoit-Maréchal, Marco Salvalaglio","doi":"10.1088/1361-651x/ad42bb","DOIUrl":"https://doi.org/10.1088/1361-651x/ad42bb","url":null,"abstract":"The Swift–Hohenberg (SH) and phase-field crystal (PFC) models are minimal yet powerful approaches for studying phenomena such as pattern formation, collective order, and defects via smooth order parameters. They are based on a free-energy functional that inherently includes elasticity effects. This study addresses how gradient elasticity (GE), a theory that accounts for elasticity effects at microscopic scales by introducing additional characteristic lengths, is incorporated into SH and PFC models. After presenting the fundamentals of these theories and models, we first calculate the characteristic lengths for various lattice symmetries in an approximated setting. We then discuss numerical simulations of stress fields at dislocations and comparisons with analytic solutions within first and second strain-gradient elasticity. Effective GE characteristic lengths for the elastic fields induced by dislocations are found to depend on the free-energy parameters in the same manner as the phase correlation length, thus unveiling how they change with the quenching depth. The findings presented in this study enable a thorough discussion and analysis of small-scale elasticity effects in pattern formation and crystalline systems using SH and PFC models and, importantly, complete the elasticity analysis therein. Additionally, we provide a microscopic foundation for GE in the context of order-disorder phase transitions.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"65 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-04-16DOI: 10.1088/1361-651x/ad3a00
Orhan Gülcan, Kadir Günaydın, Aykut Tamer
Triply periodic minimal surface (TPMS) lattices have drawn great attention both in academic and industrial perspective due to their outstanding mechanical behaviours. Additive manufacturing (AM) modalities enable the production of these lattices very easily. However, dimensional inaccuracy is still one of the problems that AM still faces with. Manufacturing of these lattices with AM modalities, then measuring the critical dimensions and making design changes accordingly is a costly process. Therefore, it is necessary to predict the dimensional deviation of TPMS lattices before print is a key topic. This study focused on prediction of dimensional deviation of laser powder bed fusion (LPBF) produced gyroid, diamond, primitive, IWP and Fisher-Koch lattices by using thermomechanical simulations. TPMS type, unit cell size, volume fraction, functional grading and part orientation were selected as design variables. Results showed that all the design inputs have effects on dimensional accuracy of LPBF produced parts and TPMS type has the most critical factor. Based on analysis of variance analysis, an optimum lattice configuration was proposed to obtain the lowest dimensional deviation after LPBF build.
{"title":"The effect of geometrical parameters on dimensional deviation in LPBF produced TPMS lattices: a numerical simulation based study","authors":"Orhan Gülcan, Kadir Günaydın, Aykut Tamer","doi":"10.1088/1361-651x/ad3a00","DOIUrl":"https://doi.org/10.1088/1361-651x/ad3a00","url":null,"abstract":"Triply periodic minimal surface (TPMS) lattices have drawn great attention both in academic and industrial perspective due to their outstanding mechanical behaviours. Additive manufacturing (AM) modalities enable the production of these lattices very easily. However, dimensional inaccuracy is still one of the problems that AM still faces with. Manufacturing of these lattices with AM modalities, then measuring the critical dimensions and making design changes accordingly is a costly process. Therefore, it is necessary to predict the dimensional deviation of TPMS lattices before print is a key topic. This study focused on prediction of dimensional deviation of laser powder bed fusion (LPBF) produced gyroid, diamond, primitive, IWP and Fisher-Koch lattices by using thermomechanical simulations. TPMS type, unit cell size, volume fraction, functional grading and part orientation were selected as design variables. Results showed that all the design inputs have effects on dimensional accuracy of LPBF produced parts and TPMS type has the most critical factor. Based on analysis of variance analysis, an optimum lattice configuration was proposed to obtain the lowest dimensional deviation after LPBF build.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"39 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-04-16DOI: 10.1088/1361-651x/ad39ff
Simon Gramatte, Vladyslav Turlo, Olivier Politano
In this study, we critically evaluate the performance of various interatomic potentials/force fields against a benchmark ab initio database for bulk amorphous alumina. The interatomic potentials tested in this work include all major fixed charge and variable charge models developed to date for alumina. Additionally, we introduce a novel machine learning interatomic potential constructed using the NequIP framework based on graph neural networks. Our findings reveal that the fixed-charge potential developed by Matsui and coworkers offers the most optimal balance between computational efficiency and agreement with ab initio data for stoichiometric alumina. Such balance cannot be provided by machine learning potentials when comparing performance with Matsui potential on the same computing infrastructure using a single Graphical Processing Unit. For non-stoichiometric alumina, the variable charge potentials, in particular ReaxFF, exhibit an impressive concordance with density functional theory calculations. However, our NequIP potentials trained on a small fraction of the ab initio database easily surpass ReaxFF in terms of both accuracy and computational performance. This is achieved without large overhead in terms of potential fitting and fine-tuning, often associated with the classical potential development process as well as training of standard deep neural network potentials, thus advocating for the use of data-efficient machine learning potentials like NequIP for complex cases of non-stoichiometric amorphous oxides.
在本研究中，我们根据针对块状无定形氧化铝的基准 ab initio 数据库，对各种原子间势/力场的性能进行了严格评估。这项工作中测试的原子间势包括迄今为止针对氧化铝开发的所有主要固定电荷和可变电荷模型。此外，我们还采用基于图神经网络的 NequIP 框架构建了一种新型机器学习原子间势。我们的研究结果表明，Matsui 及其同事开发的定电荷势在计算效率和与原子序数氧化铝的原子序数数据的一致性之间实现了最佳平衡。在使用单个图形处理单元的相同计算基础设施上比较机器学习势能与松井势能的性能时，机器学习势能无法提供这种平衡。对于非化学计量氧化铝，可变电荷势，尤其是 ReaxFF，与密度泛函理论计算的一致性令人印象深刻。然而，我们在一小部分原子序数数据库上训练的 NequIP 电位在准确性和计算性能方面都轻松超越了 ReaxFF。在实现这一目标的过程中，无需进行大量的电位拟合和微调（通常与经典的电位开发过程以及标准深度神经网络电位的训练有关），因此主张在非化学计量无定形氧化物的复杂情况下使用像 NequIP 这样数据高效的机器学习电位。
{"title":"Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database","authors":"Simon Gramatte, Vladyslav Turlo, Olivier Politano","doi":"10.1088/1361-651x/ad39ff","DOIUrl":"https://doi.org/10.1088/1361-651x/ad39ff","url":null,"abstract":"In this study, we critically evaluate the performance of various interatomic potentials/force fields against a benchmark <italic toggle=\"yes\">ab initio</italic> database for bulk amorphous alumina. The interatomic potentials tested in this work include all major fixed charge and variable charge models developed to date for alumina. Additionally, we introduce a novel machine learning interatomic potential constructed using the NequIP framework based on graph neural networks. Our findings reveal that the fixed-charge potential developed by Matsui and coworkers offers the most optimal balance between computational efficiency and agreement with <italic toggle=\"yes\">ab initio</italic> data for stoichiometric alumina. Such balance cannot be provided by machine learning potentials when comparing performance with Matsui potential on the same computing infrastructure using a single Graphical Processing Unit. For non-stoichiometric alumina, the variable charge potentials, in particular ReaxFF, exhibit an impressive concordance with density functional theory calculations. However, our NequIP potentials trained on a small fraction of the <italic toggle=\"yes\">ab initio</italic> database easily surpass ReaxFF in terms of both accuracy and computational performance. This is achieved without large overhead in terms of potential fitting and fine-tuning, often associated with the classical potential development process as well as training of standard deep neural network potentials, thus advocating for the use of data-efficient machine learning potentials like NequIP for complex cases of non-stoichiometric amorphous oxides.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"20 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-03-08DOI: 10.1088/1361-651x/ad2d67
Yunpeng Gao, Xiangguo Zeng, Minghua Chi
Repairing metal micro-defects at the atomic level is very challenging due to their random dispersion and difficulty in identification. At the same time, the interaction of hydrogen with metal may cause hydrogen damage or embrittlement, endangering structural safety. As a result, it is critical to speed up the dehydrogenation of hydrogen-containing materials. The applied electric field can repair the vacancy defects of the material and accelerate the dehydrogenation of the hydrogen-containing metal. The influence of the external environment on the diffusion coefficient of hydrogen in polycrystalline metals was researched using molecular dynamics in this article, and the mechanism of hydrogen diffusion was investigated. Simultaneously, the mechanical characteristics of Fe3Cr alloy were compared during typical heat treatment and electrical treatment. The effect of temperature, electric field strength, and electric field direction on the diffusion coefficient was investigated using orthogonal test analysis. The results demonstrate that temperature and electric field strength have a significant impact on the diffusion coefficient. The atom vibrates violently as the temperature rises, breaking past the diffusion barrier and completing the atomic transition. The addition of the electric field adds extra free energy, decreases the atom’s activation energy, and ultimately enhances the atom’s diffusion coefficient. The repair impact of vacancy defects under electrical treatment is superior to that of typical annealing treatment for polycrystalline Fe3Cr alloy. The electric field can cause the dislocation to migrate, increasing the metal’s toughness and plasticity. This research serves as a useful reference for the electrical treatment of metal materials and offers a method for the quick dehydrogenation of hydrogen-containing materials.
{"title":"Applied electric field to repair metal defects and accelerate dehydrogenation","authors":"Yunpeng Gao, Xiangguo Zeng, Minghua Chi","doi":"10.1088/1361-651x/ad2d67","DOIUrl":"https://doi.org/10.1088/1361-651x/ad2d67","url":null,"abstract":"Repairing metal micro-defects at the atomic level is very challenging due to their random dispersion and difficulty in identification. At the same time, the interaction of hydrogen with metal may cause hydrogen damage or embrittlement, endangering structural safety. As a result, it is critical to speed up the dehydrogenation of hydrogen-containing materials. The applied electric field can repair the vacancy defects of the material and accelerate the dehydrogenation of the hydrogen-containing metal. The influence of the external environment on the diffusion coefficient of hydrogen in polycrystalline metals was researched using molecular dynamics in this article, and the mechanism of hydrogen diffusion was investigated. Simultaneously, the mechanical characteristics of Fe<sub>3</sub>Cr alloy were compared during typical heat treatment and electrical treatment. The effect of temperature, electric field strength, and electric field direction on the diffusion coefficient was investigated using orthogonal test analysis. The results demonstrate that temperature and electric field strength have a significant impact on the diffusion coefficient. The atom vibrates violently as the temperature rises, breaking past the diffusion barrier and completing the atomic transition. The addition of the electric field adds extra free energy, decreases the atom’s activation energy, and ultimately enhances the atom’s diffusion coefficient. The repair impact of vacancy defects under electrical treatment is superior to that of typical annealing treatment for polycrystalline Fe<sub>3</sub>Cr alloy. The electric field can cause the dislocation to migrate, increasing the metal’s toughness and plasticity. This research serves as a useful reference for the electrical treatment of metal materials and offers a method for the quick dehydrogenation of hydrogen-containing materials.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"158 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-03-08DOI: 10.1088/1361-651x/ad2d69
Qingyu Hou, Mude Qi, Cong Li
The new magnetic mechanism and optical properties of Ga vacancies and Hi interstitial in the GaN: Be/Mg/Ca system have not been fully understood, and the use of first principles can solve this problem. The effect of point defects on the magnetic mechanism and optical properties of the GaN: Be/Mg/Ca system was investigated using the first nature principle of the hybridized generalized HSE06 method. Results show that all doped systems have N2− ions in addition to N3− ions, and N2− ions have the dual property of itinerant electrons in the off-domain (donor) and of local electrons (acceptor). The magnetism of magnetic doped systems is generated by the hybrid coupling of Ga4s and N2− 2p states. In comparison with the Ga34MN36 (M = Be/Ca) system, the magnetic moments of Ga34MHiN36 (M = Be/Ca) system are reduced after doping with Hi interstitial. The magnetic properties of the Ga34MgN36 system can be regulated by the presence or absence of Hi interstitial, which is advantageous as a magnetic switch. The absorption spectral distribution of the Ga34MgHiN36 system extends to the mid-infrared optical region. This material has some reference value as infrared thermophotovoltaic cells, infrared photodetectors, or infrared semiconductor lasers.
人们对 GaN：Be/Mg/Ca 体系中 Ga 空位和 Hi 间隙的新磁机制和光学性质还没有完全了解，而利用第一性原理可以解决这个问题。利用杂化广义 HSE06 方法的第一性原理研究了点缺陷对 GaN：Be/Mg/Ca 体系的磁机制和光学性质的影响。结果表明，所有掺杂体系中除了 N3- 离子外，还有 N2- 离子，N2- 离子具有离域巡回电子（供体）和局部电子（受体）的双重特性。掺磁系统的磁性是由 Ga4s 和 N2- 2p 态的混合耦合产生的。与 Ga34MN36（M = Be/Ca）体系相比，Ga34MHiN36（M = Be/Ca）体系在掺杂 Hi 中间体后磁矩减小。Ga34MgN36 体系的磁性能可以通过 Hi 中间体的存在或不存在来调节，这对于磁开关来说是非常有利的。Ga34MgHiN36 系统的吸收光谱分布延伸至中红外光区。这种材料作为红外热光电池、红外光探测器或红外半导体激光器具有一定的参考价值。
{"title":"First-principles study on the effect of point defects on the magnetic new mechanism and optical properties of the GaN:Be/Mg/Ca system","authors":"Qingyu Hou, Mude Qi, Cong Li","doi":"10.1088/1361-651x/ad2d69","DOIUrl":"https://doi.org/10.1088/1361-651x/ad2d69","url":null,"abstract":"The new magnetic mechanism and optical properties of Ga vacancies and H<sub>i</sub> interstitial in the GaN: Be/Mg/Ca system have not been fully understood, and the use of first principles can solve this problem. The effect of point defects on the magnetic mechanism and optical properties of the GaN: Be/Mg/Ca system was investigated using the first nature principle of the hybridized generalized HSE06 method. Results show that all doped systems have N<sup>2−</sup> ions in addition to N<sup>3−</sup> ions, and N<sup>2−</sup> ions have the dual property of itinerant electrons in the off-domain (donor) and of local electrons (acceptor). The magnetism of magnetic doped systems is generated by the hybrid coupling of Ga4s and N<sup>2−</sup> 2p states. In comparison with the Ga<sub>34</sub>MN<sub>36</sub> (M = Be/Ca) system, the magnetic moments of Ga<sub>34</sub>MH<sub>i</sub>N<sub>36</sub> (M = Be/Ca) system are reduced after doping with H<sub>i</sub> interstitial. The magnetic properties of the Ga<sub>34</sub>MgN<sub>36</sub> system can be regulated by the presence or absence of H<sub>i</sub> interstitial, which is advantageous as a magnetic switch. The absorption spectral distribution of the Ga<sub>34</sub>MgH<sub>i</sub>N<sub>36</sub> system extends to the mid-infrared optical region. This material has some reference value as infrared thermophotovoltaic cells, infrared photodetectors, or infrared semiconductor lasers.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"30 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-03-08DOI: 10.1088/1361-651x/ad2d68
Nikolay Zotov, Konstantin Gubaev, Julian Wörner, Blazej Grabowski
A new machine-learning interatomic potential, specifically a moment tensor potential (MTP), is developed for the study of screw-dislocation properties in body-centered-cubic (bcc) Nb in the thermally- and stress-assisted temperature regime. Importantly, configurations with straight screw dislocations and with kink pairs are included in the training set. The resulting MTP reproduces with near density-functional theory (DFT) accuracy a broad range of physical properties of bcc Nb, in particular, the Peierls barrier and the compact screw-dislocation core structure. Moreover, it accurately reproduces the energy of the easy core and the twinning-anti-twinning asymmetry of the critical resolved shear stress (CRSS). Thereby, the developed MTP enables large-scale molecular dynamics simulations with near DFT accuracy of properties such as for example the Peierls stress, the critical waiting time for the onset of screw dislocation movement, atomic trajectories of screw dislocation migration, as well as the temperature dependence of the CRSS. A critical assessment of previous results obtained with classical embedded atom method potentials thus becomes possible.
{"title":"Moment tensor potential for static and dynamic investigations of screw dislocations in bcc Nb","authors":"Nikolay Zotov, Konstantin Gubaev, Julian Wörner, Blazej Grabowski","doi":"10.1088/1361-651x/ad2d68","DOIUrl":"https://doi.org/10.1088/1361-651x/ad2d68","url":null,"abstract":"A new machine-learning interatomic potential, specifically a moment tensor potential (MTP), is developed for the study of screw-dislocation properties in body-centered-cubic (bcc) Nb in the thermally- and stress-assisted temperature regime. Importantly, configurations with straight screw dislocations and with kink pairs are included in the training set. The resulting MTP reproduces with near density-functional theory (DFT) accuracy a broad range of physical properties of bcc Nb, in particular, the Peierls barrier and the compact screw-dislocation core structure. Moreover, it accurately reproduces the energy of the easy core and the twinning-anti-twinning asymmetry of the critical resolved shear stress (CRSS). Thereby, the developed MTP enables large-scale molecular dynamics simulations with near DFT accuracy of properties such as for example the Peierls stress, the critical waiting time for the onset of screw dislocation movement, atomic trajectories of screw dislocation migration, as well as the temperature dependence of the CRSS. A critical assessment of previous results obtained with classical embedded atom method potentials thus becomes possible.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"33 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140314165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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