Materials Genome Engineering Advances最新文献_第3页

Enhancing creep rupture life prediction of high-temperature titanium alloys using convolutional neural networks 基于卷积神经网络的高温钛合金蠕变断裂寿命预测

Materials Genome Engineering Advances Pub Date : 2024-11-24 DOI: 10.1002/mgea.68

Bangtan Zong, Jinshan Li, Changlu Zhou, Ping Wang, Bin Tang, Ruihao Yuan

引用次数: 0

Accelerating spin Hall conductivity predictions via machine learning 通过机器学习加速自旋霍尔电导率预测

Materials Genome Engineering Advances Pub Date : 2024-10-28 DOI: 10.1002/mgea.67

Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi-Chi Zhang, Junlin Li, Xing-Qiu Chen, Peitao Liu

{"title":"Accelerating spin Hall conductivity predictions via machine learning","authors":"Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi-Chi Zhang, Junlin Li, Xing-Qiu Chen, Peitao Liu","doi":"10.1002/mgea.67","DOIUrl":"https://doi.org/10.1002/mgea.67","url":null,"abstract":"Accurately predicting the spin Hall conductivity (SHC) is crucial for designing novel spintronic devices that leverage the spin Hall effect. First-principles calculations of SHCs are computationally intensive and unsuitable for quick high-throughput screening. Here, we have developed a residual crystal graph convolutional neural network (Res-CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework. We found that Res-CGCNN surpasses CGCNN, achieving a mean absolute error of 115.4 (ℏ/e) (S/cm) for regression and an area under the receiver operating characteristic curve of 0.86 for classification. Additionally, we utilized Res-CGCNN to conduct high-throughput screenings of materials in the Materials Project database that were absent in the training set. This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000 (ℏ/e) (S/cm), which were validated through first-principles calculations. This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition, serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.67","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253756","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

Atomic characteristics of heterogeneous nucleation during solidification of aluminum alloys: A critical review 铝合金凝固过程中非均相形核的原子特征：综述

Materials Genome Engineering Advances Pub Date : 2024-10-16 DOI: 10.1002/mgea.65

Wenshuo Hao, Sida Ma, Zihui Dong, Yaowen Hu, Lijun Wang, Hao Chen, Qingyan Xu, Hongbiao Dong

{"title":"Atomic characteristics of heterogeneous nucleation during solidification of aluminum alloys: A critical review","authors":"Wenshuo Hao, Sida Ma, Zihui Dong, Yaowen Hu, Lijun Wang, Hao Chen, Qingyan Xu, Hongbiao Dong","doi":"10.1002/mgea.65","DOIUrl":"https://doi.org/10.1002/mgea.65","url":null,"abstract":"Solidification is a critical process in the manufacturing of metals and alloys, with nucleation being the initial stage that determines the resulting microstructure and mechanical properties. Among various nucleation methods, heterogeneous nucleation is particularly effective in controlling the solidified structure and properties. However, the underlying mechanisms and atomic characteristics of heterogeneous nucleation remain a topic of debate. This paper reviews recent advancements and the current state of research on heterogeneous nucleation during the solidification of aluminum alloys. It focuses on three key areas: the methods and mechanisms for influencing heterogeneous nucleation, existing theories on the subject, and recent experimental and modeling studies on the effect of atomic-scale interactions at the solid/liquid interface on nucleation. The paper also addresses the ongoing challenges and future directions, highlighting the importance of atomic-scale experimental characterization, the validity and reliability of atomic-scale simulations, the role of the pre-nucleation layer at the solid/liquid interface, and the impact of solute elements on the formation of the pre-nucleation layer.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.65","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717228","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

Multiscale simulations of amorphous and crystalline AgSnSe2 alloy for reconfigurable nanophotonic applications 用于可重构纳米光子应用的非晶和结晶AgSnSe2合金的多尺度模拟

Materials Genome Engineering Advances Pub Date : 2024-10-15 DOI: 10.1002/mgea.62

Xueyang Shen, Siyu Zhang, Yihui Jiang, Tiankuo Huang, Suyang Sun, Wen Zhou, Jiangjing Wang, Riccardo Mazzarello, Wei Zhang

{"title":"Multiscale simulations of amorphous and crystalline AgSnSe2 alloy for reconfigurable nanophotonic applications","authors":"Xueyang Shen, Siyu Zhang, Yihui Jiang, Tiankuo Huang, Suyang Sun, Wen Zhou, Jiangjing Wang, Riccardo Mazzarello, Wei Zhang","doi":"10.1002/mgea.62","DOIUrl":"https://doi.org/10.1002/mgea.62","url":null,"abstract":"Chalcogenide phase-change materials (PCM) have been explored in novel nonvolatile memory and neuromorphic computing technologies. Upon fast crystallization process, the conventional PCM undergo a semiconductor–to–semiconductor transition. However, some PCM change from a semiconducting amorphous phase to a metallic crystalline phase with low conductivity (“bad metal”). In this work, we focus on new “bad metal” PCM, namely, AgSnSe2, and carry out multiscale simulations to evaluate its potential for reconfigurable nanophotonic devices. We study the structural features and optical properties of both crystalline and amorphous AgSnSe2 via density functional theory (DFT) calculations and DFT-based ab initio molecular dynamic (AIMD) simulations. Then we use the calculated optical profiles as input parameters for finite difference time domain (FDTD) modeling of waveguide and metasurface devices. Our multiscale simulations predict AgSnSe2 to be a promising candidate for phase-change photonic applications.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.62","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717161","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 novel treatment of solute drag and solute trapping at a solid–liquid interface during rapid solidification of multicomponent alloys 多组分合金快速凝固过程中固液界面溶质拖拽和溶质捕获的新处理方法

Materials Genome Engineering Advances Pub Date : 2024-10-15 DOI: 10.1002/mgea.60

Qiang Du

{"title":"A novel treatment of solute drag and solute trapping at a solid–liquid interface during rapid solidification of multicomponent alloys","authors":"Qiang Du","doi":"10.1002/mgea.60","DOIUrl":"https://doi.org/10.1002/mgea.60","url":null,"abstract":"In response to the renewed interest in solute drag and solute trapping models fueled by their applications to additive manufacturing, a novel treatment is proposed to describe the diffusional behaviors of solute at a migrating solid–liquid interface during rapid solidification of multicomponent alloys. While the treatment is still built on irreversible thermodynamics and linear kinetic law, its novelty lies in breaking up the classical trans-interface diffusional flux into two separate fluxes one is the transferred-back flux with its ending point at the interface and the other is the bumping-back flux with its starting point at the interface. This novel treatment entails three significant improvements in reference to the existing models. Firstly, it reveals that the degree of solute drag is dependent on the ratio of liquid diffusive speed over interface diffusive speed. Secondly, a novel relation between the distribution coefficient and interface velocity is derived. It amends the confusing behavior seen in Aziz’s without-drag continuous growth model. Thirdly, the proposed treatment eliminates the need of prescribing the degree of solute drag parameter for the kinetic phase diagram calculation. The numerical solution to the proposed model is presented, and it is ready to be used for the kinetic phase diagram calculation.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.60","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717050","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

Machine-learning-assisted intelligent synthesis of UiO-66(Ce): Balancing the trade-off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene 机器学习辅助智能合成 UiO-66(Ce)：平衡结构缺陷与热稳定性之间的权衡，实现双环戊二烯的高效氢化

Materials Genome Engineering Advances Pub Date : 2024-09-07 DOI: 10.1002/mgea.61

Jing Lin, Tao Ban, Tian Li, Ye Sun, Shenglan Zhou, Rushuo Li, Yanjing Su, Jitti Kasemchainan, Hongyi Gao, Lei Shi, Ge Wang

{"title":"Machine-learning-assisted intelligent synthesis of UiO-66(Ce): Balancing the trade-off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene","authors":"Jing Lin, Tao Ban, Tian Li, Ye Sun, Shenglan Zhou, Rushuo Li, Yanjing Su, Jitti Kasemchainan, Hongyi Gao, Lei Shi, Ge Wang","doi":"10.1002/mgea.61","DOIUrl":"https://doi.org/10.1002/mgea.61","url":null,"abstract":"Metal-organic frameworks (MOFs), renowned for structural diversity and design flexibility, exhibit potential in catalysis. However, the pursuit of higher catalytic activity through defects often compromises stability, requiring a delicate balance. Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient. Herein, taking the typical MOF UiO-66(Ce) as an illustrative example, a closed loop workflow is built, which integrates machine learning (ML)-assissted prediction, multi-objective optimization (MOO) and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce) for efficient hydrogenation of dicyclopentadiene (DCPD). An automatic data extraction program ensures data accuracy, establishing a high-quality database. ML is employed to explore the intricate synthesis-structure-property correlations, enabling precise delineation of pure-phase subspace and accurate predictions of properties. After two iterations, MOO model identifies optimal protocols for high defect content (>40%) and thermal stability (>300°C). The optimized UiO-66(Ce) exhibits superior catalytic performance in hydrogenation of DCPD, validating the precision and reliability of our methodology. This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.61","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429319","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

High-throughput study of X-ray-induced synthesis of flower-like CuxO X 射线诱导合成花状 CuxO 的高通量研究

Materials Genome Engineering Advances Pub Date : 2024-08-20 DOI: 10.1002/mgea.59

Qingyun Hu, Lingyue Zhu, Genmao Zhuang, Hong Wang, Yang Ren, Jian Hui

{"title":"High-throughput study of X-ray-induced synthesis of flower-like CuxO","authors":"Qingyun Hu, Lingyue Zhu, Genmao Zhuang, Hong Wang, Yang Ren, Jian Hui","doi":"10.1002/mgea.59","DOIUrl":"https://doi.org/10.1002/mgea.59","url":null,"abstract":"CuxO with flower-like hierarchical structures has attracted significant research interest due to its intriguing morphologies and unique properties. The conventional methods for synthesizing such complex structures are costly and require rigorous experimental conditions. Recently, the X-ray irradiation has emerged as a promising method for the rapid fabrication of precisely controlled CuxO shapes in large areas under environmentally friendly conditions. Nevertheless, the morphological regulation of the X-ray-induced synthesis of the CuxO is a multi-parameter optimization task. Therefore, it is essential to quantitatively reveal the interplay between these parameters and the resulting morphology. In this work, we employed a high-throughput experimental data-driven approach to investigate the kinetics of X-ray-induced reactions and the impact of key factors, including sputtering power, film thickness, and annealing of precursor Cu thin films on the morphologies of CuxO. For the first time, the flower-like CuxO nanostructures were synthesized using X-ray radiation at ambient condition. This research proposes an eco-friendly and cost-effective strategy for producing CuxO with customizable morphologies. Furthermore, it enhances comprehension of the underlying mechanisms of X-ray-induced morphological modification, which is essential for optimizing the synthesis process and expanding the potential applications of flower-like structures.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.59","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430136","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

Systematic assessment of various universal machine-learning interatomic potentials 对各种通用机器学习原子间势能的系统评估

Materials Genome Engineering Advances Pub Date : 2024-07-31 DOI: 10.1002/mgea.58

Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian-Marco Rignanese

{"title":"Systematic assessment of various universal machine-learning interatomic potentials","authors":"Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian-Marco Rignanese","doi":"10.1002/mgea.58","DOIUrl":"https://doi.org/10.1002/mgea.58","url":null,"abstract":"Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab initio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate four different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.58","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430404","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 a two-dimensional bipolar electrochemistry technique for high throughput corrosion screening 开发用于高通量腐蚀筛选的二维双极电化学技术

Materials Genome Engineering Advances Pub Date : 2024-07-19 DOI: 10.1002/mgea.57

Yiqi Zhou, Dirk Lars Engelberg

引用次数: 0

Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era 人工智能支持先进材料的智能设计和制造：人工智能+时代的无尽前沿

Materials Genome Engineering Advances Pub Date : 2024-07-16 DOI: 10.1002/mgea.56

William Yi Wang, Suyang Zhang, Gaonan Li, Jiaqi Lu, Yong Ren, Xinchao Wang, Xingyu Gao, Yanjing Su, Haifeng Song, Jinshan Li

{"title":"Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era","authors":"William Yi Wang, Suyang Zhang, Gaonan Li, Jiaqi Lu, Yong Ren, Xinchao Wang, Xingyu Gao, Yanjing Su, Haifeng Song, Jinshan Li","doi":"10.1002/mgea.56","DOIUrl":"https://doi.org/10.1002/mgea.56","url":null,"abstract":"Future-oriented Science & Technology (S&T) Strategies trigger the innovative developments of advanced materials, providing an envision to the significant progress of leading-/cutting-edge science, engineering, and technologies for the next few decades. Motivated by Made in China 2025 and New Material Power Strategy by 2035, several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data, database, standards, and ecosystems are discussed. Referring to classical toolkits at various spatial and temporal scales, AI-based toolkits and AI-enabled computations for material design are compared, highlighting the dominant role of the AI agent paradigm. Our recent developed ProME platform together with its functions is introduced briefly. A case study of AI agent assistant welding is presented, which is consisted of the large language model, auto-coding via AI agent, image processing, image mosaic, and machine learning for welding defect detection. Finally, more duties are called to educate the next generation workforce with creative minds and skills. It is believed that the transformation of knowledge-enabled data-driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.56","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430056","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