Computational Materials Science最新文献_第7页

Interfacial thermal conductance in 2D WS2/MoSe2 and MoS2/MoSe2 lateral heterostructures 二维 WS2/MoSe2 和 MoS2/MoSe2 横向异质结构中的界面热导率

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-19 DOI: 10.1016/j.commatsci.2024.113282

{"title":"Interfacial thermal conductance in 2D WS2/MoSe2 and MoS2/MoSe2 lateral heterostructures","authors":"","doi":"10.1016/j.commatsci.2024.113282","DOIUrl":"10.1016/j.commatsci.2024.113282","url":null,"abstract":"<div>In-plane Transition Metal Dichalcogenides (TMDs) heterostructures hold immense potential for various applications in the modern semiconductor industry, including electronics, optoelectronics, and photovoltaic devices. Different TMD monolayers can be ‘stitched’ together to construct an in-plane (lateral) heterostructure. As different TMD monolayers present different work functions and have their intrinsic shortcomings, a TMD heterostructure is an excellent form to optimize their properties and to achieve the best functionality. This requires a quantitative understanding of the properties of the interfaces in the heterostructures. In this work, we perform nonequilibrium molecular dynamics simulations, based on a parametrized Stillinger-Weber potential, to investigates the thermal conductance of the interfaces in 2D <math><mrow><msub><mrow><mi>WS</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>MoSe</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math> and <math><mrow><msub><mrow><mi>MoS</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>MoSe</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math> in-plane heterostructures, as well as in 2D lateral <math><mrow><msub><mrow><mi>WS</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>MoSe</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math> superlattices. Three distinct types of interfaces, including defect-free coherent interfaces, interfaces with the <math><mrow><mn>5</mn><mo>∣</mo><mn>7</mn></mrow></math> defects, and the alloy-like incoherent interfaces, are explored. The effects of interphase structure and temperature are quantified. Phonon density of states (PDOS) analysis is used to understand the effect of different interphase structures. The effect of superlattice period on thermal conductance of the superlattices has also been quantified.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006591","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}

引用次数: 0

Optimizing SEM parameters for segmentation with AI – Part 2: Designing and training a regression model 利用人工智能优化细分市场的 SEM 参数 - 第 2 部分：设计和训练回归模型

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-17 DOI: 10.1016/j.commatsci.2024.113283

{"title":"Optimizing SEM parameters for segmentation with AI – Part 2: Designing and training a regression model","authors":"","doi":"10.1016/j.commatsci.2024.113283","DOIUrl":"10.1016/j.commatsci.2024.113283","url":null,"abstract":"<div>Selecting the best microscope parameters for optimal image quality currently relies on microscopists; there exist no procedures or guidelines for tuning parameters to ensure the desired image quality is achieved. More importantly, for quantitative analysis purposes, adequate image quality for segmentation should be prioritized. This paper is the second of two parts, describing a regression model, mixed input, multiple output with Keras TensorFlow, trained to predict the beam energy and probe current, two important parameters for image quality. Specifically, parameters are predicted to optimize the image quality for segmentation, using a generated training set, as described in Part 1 of this paper. Model performance is then tested on models trained with multiple different training sets, and with different proportions of simulated and acquired data. First, to examine the impact of the training set on the prediction accuracy and then, to evaluate the importance of including real data during training. The model successfully predicted the beam energy and probe current to set on the microscope to improve image quality for segmentation. Models trained with both simulated and acquired data performed the best, as evaluated by their efficacy at improving the image quality for feature segmentation.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624005044/pdfft?md5=91d8ca5b2512ebcf564583e96b35d0d1&pid=1-s2.0-S0927025624005044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148902","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}

引用次数: 0

Giant tunnel magnetoresistance in in-plane magnetic tunnel junctions based on the heterointerface-induced half-metallic 2H-VS2 基于异质面诱导半金属 2H-VS2 的面内磁隧道结中的巨隧道磁阻

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-17 DOI: 10.1016/j.commatsci.2024.113290

{"title":"Giant tunnel magnetoresistance in in-plane magnetic tunnel junctions based on the heterointerface-induced half-metallic 2H-VS2","authors":"","doi":"10.1016/j.commatsci.2024.113290","DOIUrl":"10.1016/j.commatsci.2024.113290","url":null,"abstract":"<div>Magnetic tunnel junctions (MTJs) constructed from atomically thin two-dimensional (2D) magnetic materials have attracted great attention in recent years because it meets the requirements of miniaturization and high tunability of next-generation spintronic devices. In this work, we demonstrate that the ferromagnetic semiconductor VS<math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math> is transformed into a half-metal in VS<math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math>/MoSSe vdW heterostructure. Based on the heterostructure, we design an in-plane MTJs that comprise a monolayer VS<math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math> barrier sandwiched between two VS<math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math>/MoSSe heterostructure electrodes. Through density functional calculations combined with a nonequilibrium Green’s function technique, it is found that the tunnel magnetoresistance (TMR) ratio as high as 4.35 × 10<math><mrow><msup><mrow></mrow><mrow><mn>9</mn></mrow></msup><mtext>%</mtext></mrow></math> can be achieved. Moreover, the TMR ratio can be tuned by the barrier length, and the maximum value exceeds 10<math><mrow><msup><mrow></mrow><mrow><mn>15</mn></mrow></msup><mtext>%</mtext></mrow></math>. These results not only provide a novel route for designing MTJs using 2D ferromagnetic semiconductor material, but also demonstrate the great importance of vdW heterostructures in the design of spintronic devices.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998201","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}

引用次数: 0

Liquid thermophysical properties of Ag-Si alloy based on deep learning potential 基于深度学习潜能的银硅合金液态热物理特性

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-16 DOI: 10.1016/j.commatsci.2024.113293

引用次数: 0

A comparative first-principles study on the physical properties of Gd2Zr2O7 weberite and pyrochlore 关于 Gd2Zr2O7 韦伯岩和火成岩物理性质的第一性原理比较研究

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-15 DOI: 10.1016/j.commatsci.2024.113285

引用次数: 0

Optimizing SEM parameters for segmentation with AI – Part 1: Generating a training set 利用人工智能优化 SEM 参数以进行细分 - 第 1 部分：生成训练集

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-15 DOI: 10.1016/j.commatsci.2024.113255

{"title":"Optimizing SEM parameters for segmentation with AI – Part 1: Generating a training set","authors":"","doi":"10.1016/j.commatsci.2024.113255","DOIUrl":"10.1016/j.commatsci.2024.113255","url":null,"abstract":"<div>Extracting significant quantitative results from SEM images requires feature segmentation with image processing software. The efficiency of segmentation algorithms depends on the image quality, determined by the parameters set on the microscope during acquisitions. By integrating AI within SEM acquisition workflows, it is possible to suggest microscope parameters that will produce images where the features to quantify will be easily segmented. Specifically, a model is trained to automatically suggest the beam energy and probe current to set on the microscope during acquisitions. This paper is the first of two parts, describing workflows for generating a complete training set. The training set is carefully designed, consisting of both simulated data and real data acquired on the SEM by varying the energy and current. Separate workflows are required for generating simulated and acquired training examples. Simulated data generation is accomplished with the MC X-ray simulator in Dragonfly, where multiple virtual samples are created to intentionally diversify the training set. Acquiring data on the SEM for training is a time-consuming process when compared to generating simulations and would ideally be avoided but is included here to determine the degree to which it is required. Using only simulated data for adequate training, we show that our data generation workflow can be fully automated and produces a considerable amount of high quality data rapidly and with minimal effort.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624004762/pdfft?md5=b43d4f27c93183c797ee3edf30d62838&pid=1-s2.0-S0927025624004762-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148901","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}

引用次数: 0

High-precision corrosion degree nondestructive segmentation method with virtual and real synthetic data labeled by unsupervised learning 利用无监督学习标记虚拟和真实合成数据的高精度腐蚀度无损分割方法

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-15 DOI: 10.1016/j.commatsci.2024.113276

{"title":"High-precision corrosion degree nondestructive segmentation method with virtual and real synthetic data labeled by unsupervised learning","authors":"","doi":"10.1016/j.commatsci.2024.113276","DOIUrl":"10.1016/j.commatsci.2024.113276","url":null,"abstract":"<div>Corrosion is a significant issue for materials, leading to economic losses and potential safety accidents. Corrosion degree detection allows the assessment of its impact on materials, providing crucial safety and performance information essential for maintaining and managing asset integrity. This study proposes an intelligent detection technology based on the pixel-level location of surface corrosion area and corrosion degree recognition of carbon steel samples. First, a corrosion acceleration test was employed to corrode the samples to various degrees. A generative adversarial network (GAN), StyleGAN3-t expands the corrosion image, reducing the experimental workload and sample requirements. A semi-automatic labeling approach using the Segment Anything Model (SAM) was introduced for rapid and high-resolution identification of corroded regions with complex shapes. Lastly, this paper presents the MN-DeepLabv3, which replaces the DeepLabv3 backbone network Xception with MobileNetV2, for training real corroded and generated virtual images, respectively. Experiments show that MN-DeepLabv3 outperforms other algorithms in segmenting the corrosion area and recognizing the corrosion degree. This approach presents a promising technical strategy for intelligent detection of carbon steel surface corrosion.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991231","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}

引用次数: 0

Theoretical exploration of AlB2 monolayer with high energy storage properties in the field of ion battery materials 离子电池材料领域具有高储能特性的 AlB2 单层理论探索

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-14 DOI: 10.1016/j.commatsci.2024.113291

引用次数: 0

Computational approach to modeling electronic properties of titanium oxynitride systems 氧化钛系统电子特性建模的计算方法

IF 3.1 3区材料科学

Computational Materials Science Pub Date : 2024-08-14 DOI: 10.1016/j.commatsci.2024.113292

{"title":"Computational approach to modeling electronic properties of titanium oxynitride systems","authors":"","doi":"10.1016/j.commatsci.2024.113292","DOIUrl":"10.1016/j.commatsci.2024.113292","url":null,"abstract":"<div>The study presents the use of a novel on-lattice sampling approach to generate titanium oxynitride (TiNxOy) structures with potential applications in photovoltaic and water splitting. This approach presents a simple route to overcome challenges with structure-generating tools like Cluster Approach to Statistical Mechanics (CASM), and Ab initio Random Structure Search (AIRSS), CASM faces difficulty in generating ternary structures with large unit cells. With AIRSS, there is an increase in probability of sampling amorphous sample spaces with increased number of atoms in the unit cell. Here an on-lattice sampling approach was used to model the electronic structure of TiNxOy as a function of composition. We present results for Ti2N2O, Ti5N4O4 and Ti7N4O8, with 33 %, 50 % and 67 % N replaced by O via substitution relative to titanium nitride (TiN), respectively. Koopmans theorem was used correct the Kohn-Sham Density Functional Theory (KS-DFT) bandgaps with corresponding values of 2.68 eV, 3.03 eV, and 3.65 eV for 33, 50 and 67 % O doping respectively. The projected density of states (PDOS) plot for TiN shows that the Fermi level is dominated by the 3d atomic orbitals of Ti, confirming pure TiN’s metallicity. The valence bands of TiNxOy structures were dominated by 2p orbitals of O at lower energy levels, but they were dominated by 2p orbitals of N at energies close to the valence band maximum (VBM). The conduction bands were dominated by the 3d atomic orbitals of Ti, with the bandgap increasing with O composition leading to creation of shallow trap states near the VBM, which negatively impacts carrier mobility. In conclusion, the on-lattice sampling approach is an effective tool to generate highly crystalline structures of large unit cells, also keeping O substitution for N below 33 % as seen in Ti2N2O is crucial for avoiding shallow traps in TiNxOy structures.</div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985229","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}

引用次数: 0

Pressure-Induced High-Energy-Density BeN6 Materials: First-Principles study 压力诱导的高能量密度 BeN6 材料：第一原理研究

IF 3.3 3区材料科学

Computational Materials Science Pub Date : 2024-08-13 DOI: 10.1016/j.commatsci.2024.113272

Xunjiang Zhang, Huafeng Dong, Le Huang, Hui Long, Xin Zhang, Fugen Wu, Zhongfei Mu, Minru Wen

引用次数: 0