Materials Genome Engineering Advances最新文献_第6页

The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations 基于ab initio分子动力学模拟的镁合金在水中的阳极溶解动力学

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.47

Jieqiong Yan, Xinchen Xu, Gaoning Shi, Yaowei Wang, Chaohong Guan, Yuyang Chen, Yao Yang, Tao Ying, Hong Zhu, Qingli Tang, Xiaoqin Zeng

{"title":"The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations","authors":"Jieqiong Yan, Xinchen Xu, Gaoning Shi, Yaowei Wang, Chaohong Guan, Yuyang Chen, Yao Yang, Tao Ying, Hong Zhu, Qingli Tang, Xiaoqin Zeng","doi":"10.1002/mgea.47","DOIUrl":"10.1002/mgea.47","url":null,"abstract":"The corrosion susceptibility of magnesium (Mg) alloys presents a significant challenge for their broad application. Although there have been extensive experimental and theoretical investigations, the corrosion mechanisms of Mg alloys are still unclear, especially the anodic dissolution process. Here, a thorough theoretical investigation based on ab initio molecular dynamics and metadynamics simulations has been conducted to clarify the underlying corrosion mechanism of Mg anode and propose effective strategies for enhancing corrosion resistance. Through comprehensive analyses of interfacial structures and equilibrium potentials for Mg(0001)/H2O interface models with different water thicknesses, the Mg(0001)/72 H2O model is identified to be reasonable with −2.17 V vs. standard hydrogen electrode equilibrium potential. In addition, utilizing metadynamics, the free energy barrier for Mg dissolution is calculated to be 0.835 eV, enabling the theoretical determination of anodic polarization curves for pure Mg that aligns well with experimental data. Based on the Mg(0001)/72 H2O model, we further explore the effects of various alloying elements on anodic corrosion resistance, among which Al and Mn alloying elements are found to enhance corrosion resistance of Mg. This study provides valuable atomic-scale insights into the corrosion mechanism of magnesium alloys, offering theoretical guidance for developing novel corrosion-resistant Mg alloys.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141388167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning-based crystal graph network and its application in development of functional materials 基于机器学习的晶体图网络及其在功能材料开发中的应用

Materials Genome Engineering Advances Pub Date : 2024-06-04 DOI: 10.1002/mgea.38

Gang Xu, You Xue, Xiaoxiao Geng, Xinmei Hou, Jinwu Xu

{"title":"A machine learning-based crystal graph network and its application in development of functional materials","authors":"Gang Xu, You Xue, Xiaoxiao Geng, Xinmei Hou, Jinwu Xu","doi":"10.1002/mgea.38","DOIUrl":"10.1002/mgea.38","url":null,"abstract":"An active area of MGI (Materials Genome Initiative)/MGE (Materials Genome Engineering) is to accelerate the development of new materials by means of active learning and “digital trial-error” using a prediction model of material property. Machine learning methods have widely been employed for predicting crystalline materials properties with crystal graph neural networks (CGNN). The prediction accuracy of the state-of-the-art (SOTA) CGNN models based on big models and big data is generally higher. However, for the development of some classes of materials, the datasets obtained by experiments are usually lacking due to costly experiments and measurement costs. The lack of datasets will impact the accuracy of CGNN models and may result in overfitting during training models. This paper proposes a simplified crystal graph convolutional neural network (S-CGCNN) which possesses higher prediction accuracy while reducing the vast amount of train datasets and computation costs. The S-CGCNN model has successfully predicted properties of crystalline materials, such as piezoelectric materials and dielectric materials, and increased the prediction accuracy up to 12%–20% than existing SOTA CGNN models. Furthermore, the distribution map between properties and compositions of materials has been built to screen the latent space of candidate materials efficiently by principal component analysis.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated unified phase-field modeling (UPFM) 综合统一相场建模（UPFM）

Materials Genome Engineering Advances Pub Date : 2024-06-02 DOI: 10.1002/mgea.44

Yuhong Zhao

引用次数: 0

Optimal design of high-performance rare-earth-free wrought magnesium alloys using machine learning 利用机器学习优化设计高性能无稀土锻造镁合金

Materials Genome Engineering Advances Pub Date : 2024-05-31 DOI: 10.1002/mgea.45

Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

{"title":"Optimal design of high-performance rare-earth-free wrought magnesium alloys using machine learning","authors":"Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin","doi":"10.1002/mgea.45","DOIUrl":"https://doi.org/10.1002/mgea.45","url":null,"abstract":"In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare-earth-free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. The SVR model combined with multi-objective genetic algorithm are successfully used to optimize mechanical properties of four extruded alloys from Mg-Ca, Mg-Ca-Zn, Mg-Ca-Mn-Sn, and Mg-Ca-Al-Zn-Mn series alloys, respectively, and the corresponding experimental results are in good agreement with the designed ones. Furthermore, new composition schemes are proposed from a wider range of elements and processing features to match the objectives of high-strength, strength–ductility balanced, and high-ductility Mg alloys, and the four-, five- and six-element alloying schemes are provided for the candidates of new-generation wrought Mg alloys.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.45","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of thermodynamic database of the Mn-RE (RE = rare earth metals) binary systems 开发 Mn-RE（RE = 稀土金属）二元体系热力学数据库

Materials Genome Engineering Advances Pub Date : 2024-05-31 DOI: 10.1002/mgea.39

Hongjian Ye, Jiang Wang, Qing Chen, Guanghui Rao, Huaiying Zhou

{"title":"Development of thermodynamic database of the Mn-RE (RE = rare earth metals) binary systems","authors":"Hongjian Ye, Jiang Wang, Qing Chen, Guanghui Rao, Huaiying Zhou","doi":"10.1002/mgea.39","DOIUrl":"https://doi.org/10.1002/mgea.39","url":null,"abstract":"In this work, eight Mn-RE (RE = Ce, Pr, Sm, Tb, Er, Tm, Lu, and Y) binary systems were reassessed thermodynamically by the CALPHAD method based on the reported optimizations and experimental information. Self-consistent thermodynamic parameters to describe Gibbs energies of various phases in eight Mn-RE binary systems were obtained. The calculated phase equilibria and thermodynamic properties of eight Mn-RE binary systems are in good accordance with the experimental results. Furthermore, phase equilibria and thermodynamic properties of 13 Mn-RE (RE = La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Lu, and Y) binary systems were discussed systematically in combination with the present calculations and the reported optimizations. A trend was found for the variation of phase equilibria and thermodynamic properties of the Mn-RE binary systems. In general, as the RE atomic number increases, the enthalpies of mixing of liquid alloys as well as the enthalpies of formation of the intermetallic compounds become increasingly negative, and the formation temperatures of the intermetallic compounds become higher. The results provide a complete set of self-consistent thermodynamic parameters for the Mn-RE binary systems, and a thermodynamic database of 13 Mn-RE binary systems was finally achieved.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.39","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the effect of cooling rates and initial hydrogen concentrations on porosity formation in Al-Si castings 预测冷却速度和初始氢浓度对铝硅铸件中气孔形成的影响

Materials Genome Engineering Advances Pub Date : 2024-05-30 DOI: 10.1002/mgea.37

Qinghuai Hou, Junsheng Wang, Yisheng Miao, Xingxing Li, Xuelong Wu, Zhongyao Li, Guangyuan Tian, Decai Kong, Xiaoying Ma, Haibo Qiao, Wenbo Wang, Yuling Lang

{"title":"Predicting the effect of cooling rates and initial hydrogen concentrations on porosity formation in Al-Si castings","authors":"Qinghuai Hou, Junsheng Wang, Yisheng Miao, Xingxing Li, Xuelong Wu, Zhongyao Li, Guangyuan Tian, Decai Kong, Xiaoying Ma, Haibo Qiao, Wenbo Wang, Yuling Lang","doi":"10.1002/mgea.37","DOIUrl":"https://doi.org/10.1002/mgea.37","url":null,"abstract":"Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation. In this study, a coupled three-dimensional cellular automata (CA) model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration. By quantifying the pore characteristics, it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98 μm and the pore number density increases from 10.3 to 26.6 mm−3 as the cooling rate changes from 2.6 to 19.4°C/s at the initial hydrogen concentration of 0.25 mL/100 g. It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable. In addition, the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time, matching well with experiments. This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al-Si alloys.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.37","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of advanced steels by integrated computational materials engineering 通过综合计算材料工程学设计先进钢材

Materials Genome Engineering Advances Pub Date : 2024-05-20 DOI: 10.1002/mgea.36

Xiao-Gang Lu, Yanlin He, Weisen Zheng

{"title":"Design of advanced steels by integrated computational materials engineering","authors":"Xiao-Gang Lu, Yanlin He, Weisen Zheng","doi":"10.1002/mgea.36","DOIUrl":"10.1002/mgea.36","url":null,"abstract":"The integrated computational materials engineering (ICME) has achieved great success in accelerating the rational design and deployment of new materials. It is a new route of designing new materials and processes and highlighted by Materials Genome Initiative/Engineering that stresses the high-throughput computation in addition to high-throughput experimentation and materials informatics. This article presents a brief review on the basic theories and multi-scale computational tools of ICME to design advanced steel grades, including the first-principles calculations, the CALPHAD method (i.e., computational thermodynamics) fueled by dedicated databases, diffusion and phase-field simulations, as well as finite analysis methods and machine learning. In the ICME scheme to deal with steels, the CALPHAD method is considered as the core to readily consider multi-component systems and integrated to link the microscopic simulations (such as diffusion and phase field method to predict microstructure evolutions in response to external conditions) and macroscopic finite analysis method to deal with mechanical properties. Two applications are also presented to address the new routes to carry out materials design, especially for advanced steels.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.36","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble learning strategy for multi-source hydrogen embrittlement data by introducing missing information 引入缺失信息的多源氢脆数据集合学习策略

Materials Genome Engineering Advances Pub Date : 2024-05-01 DOI: 10.1002/mgea.35

Xujie Gong, Ruichao Lei, Ruize Sun, Xue Jiang, Yanjing Su, Yu Yan

{"title":"An ensemble learning strategy for multi-source hydrogen embrittlement data by introducing missing information","authors":"Xujie Gong, Ruichao Lei, Ruize Sun, Xue Jiang, Yanjing Su, Yu Yan","doi":"10.1002/mgea.35","DOIUrl":"10.1002/mgea.35","url":null,"abstract":"Accurately and quickly predicting hydrogen embrittlement performance is critical for the service of metal materials. However, due to multi-source heterogeneity, existing hydrogen embrittlement data are missing, making it impractical to train reliable machine learning models. In this study, we proposed an ensemble learning training strategy for missing data based on the Adaboost algorithm. This method introduced a mask matrix with missing data and enabled each round of training to generate sub-datasets, considering missing value information. The strategy first trained a subset of features based on the existing dataset and a selected method and continuously focused on the combination of features with the highest error for iterative training, where the mask matrix of the missing data was used as the input to fit the weights of each base learner using a neural network. Compared with directly modeling on highly sparse data, the predictive ability of this strategy was significantly improved by approximately 20%. In addition, in the testing of new samples, the predicted mean absolute error of the new model was successfully reduced from 0.2 to 0.09. This strategy offers good adaptability to the hydrogen embrittlement sensitivity of different sizes and can avoid interference from feature importance caused by filling data.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141044440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unexpected nucleation mechanism of T1 precipitates by Eshelby inclusion with unstable stacking faults 带有不稳定堆积断层的埃谢尔比包涵体产生 T1 沉淀的意外成核机制

Materials Genome Engineering Advances Pub Date : 2024-04-01 DOI: 10.1002/mgea.33

Shuo Wang, Junsheng Wang, Chengpeng Xue, Xinghai Yang, Guangyuan Tian, Hui Su, Yisheng Miao, Quan Li, Xingxing Li

引用次数: 0

Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels 在双相钢弹塑性机理的非参数逆可视化中运用深度学习

Materials Genome Engineering Advances Pub Date : 2024-03-18 DOI: 10.1002/mgea.29

Siyu Han, Chenchong Wang, Yu Zhang, Wei Xu, Hongshuang Di

{"title":"Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels","authors":"Siyu Han, Chenchong Wang, Yu Zhang, Wei Xu, Hongshuang Di","doi":"10.1002/mgea.29","DOIUrl":"https://doi.org/10.1002/mgea.29","url":null,"abstract":"Enhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non-uniform and nonlinear multisystem dual-phase steel materials and achieve an inverse analysis of the elastic-plastic mechanism. This study demonstrates that the developed CNN model achieves an accuracy of 94% in predicting the stress-strain curves of dual-phase steel microstructures with different compositions and processes, with the mean absolute error not exceeding 50 MPa, representing merely 5.26% of the average tensile strength of dual-phase steels in the dataset. The reverse visualization results of the CNN model indicate that, during tensile deformation, the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip. This results in a significant stress concentration at the grain boundaries, with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase. Moreover, compared with traditional crystal plasticity models, the CNN model exhibits a substantial improvement in computational efficiency. This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.29","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140209657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0