Computational Materials Science最新文献_第9页

A GPT-assisted iterative method for extracting domain knowledge from a large volume of literature of electromagnetic wave absorbing materials with limited manually annotated data 在人工标注数据有限的情况下，从大量电磁波吸收材料文献中提取领域知识的 GPT 辅助迭代法

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-05 DOI: 10.1016/j.commatsci.2024.113431

Dongbo Dai , Guangjie Zhang , Xiao Wei , Yudian Lin , Mengmeng Dai , Junjie Peng , Na Song , Zheng Tang , Shengzhou Li , Jiwei Liu , Yan Xu , Renchao Che , Huiran Zhang

Research on electromagnetic wave absorbing materials is an important part of materials science. Each year, a substantial amount of academic literature is published in this field, containing critical information. Rapid and effective knowledge extraction from these documents is key to accelerating field development, and automated knowledge extraction based on deep learning provides a solution to this challenge. However, deep learning models typically require extensive annotated data for training, which is time-consuming and expensive to obtain in highly specialized subfields. To address this issue, this paper presents a GPT-assisted iterative training method that uses only 30 manually annotated literature abstracts as a training set and ultimately achieves an F1 score of 82.94% for a named entity recognition model (NER). The effectiveness of this model is demonstrated by comparing with other large language models commonly used in materials science on a custom dataset. We constructed a knowledge extraction framework centered around the obtained NER model and collected literature on electromagnetic wave absorbing materials from the last decade. The extraction and application results demonstrate the practicality of our framework.

电磁波吸收材料研究是材料科学的重要组成部分。每年，该领域都会发表大量学术文献，其中包含重要信息。从这些文献中快速有效地提取知识是加速领域发展的关键，而基于深度学习的自动知识提取为这一挑战提供了解决方案。然而，深度学习模型通常需要大量带注释的数据进行训练，而在高度专业化的子领域中，获取这些数据既耗时又昂贵。为解决这一问题，本文提出了一种 GPT 辅助迭代训练方法，该方法仅使用 30 篇人工标注的文献摘要作为训练集，最终使命名实体识别模型（NER）的 F1 得分达到 82.94%。通过在定制数据集上与材料科学领域常用的其他大型语言模型进行比较，证明了该模型的有效性。我们以获得的 NER 模型为中心构建了一个知识提取框架，并收集了近十年来有关电磁波吸收材料的文献。提取和应用结果证明了我们框架的实用性。

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引用次数: 0

The first principle calculations of band offsets among Cs2(Ti, Zr, Hf)X6 double halide perovskites Cs2(Ti, Zr, Hf)X6 双卤化物包晶带偏移的第一原理计算

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-04 DOI: 10.1016/j.commatsci.2024.113436

Yongyut Laosiritaworn, Atchara Punya Jaroenjittichai

This study investigates the band offsets among Cs₂(Ti, Zr, Hf)X₆ double halide perovskites. Valence band offsets (VBO) and conduction band offsets (CBO) were calculated using density functional theory (DFT) with the Perdew–Burke-Ernzerhof (PBE) functional and the hybrid Heyd–Scuseria–Ernzerhof (HSE) functional, employing the supercell technique. This technique offers greater accuracy and reliability by explicitly calculating the potential differences at the interface. A critical factor influencing the results is the contribution of the dipole potential (V_D), which induces shifts in the VBO and CBO by approximately 0.2 to 0.6 eV relative to values predicted by the electron affinity rule. This discrepancy arises from the inclusion of interface-specific effects, such as charge redistribution and polarization. Additionally, the findings indicate that the energy band alignments among these compounds are type-I within the same group of halides, with nearly identical lattice constants for Zr- and Hf-based compounds due to their similar ionic radii. These results provide valuable insights for the design of heterostructures in electronic applications and highlight the potential of Cs₂(Ti, Zr, Hf)X₆ compounds as efficient materials for solar cells, light-emitting diodes, and photodetectors.

本研究探讨了 Cs2(Ti,Zr,Hf)X6 双卤化物包晶的能带偏移。研究采用密度泛函理论（DFT），使用 Perdew-Burke-Ernzerhof (PBE) 函数和混合 Heyd-Scuseria-Ernzerhof (HSE) 函数，利用超级电池技术计算了价带偏移（VBO）和导带偏移（CBO）。这种技术通过明确计算界面上的电位差，提供了更高的精度和可靠性。影响结果的一个关键因素是偶极电势（VD）的贡献，它导致 VBO 和 CBO 与电子亲和力规则预测值相比发生约 0.2 至 0.6 eV 的偏移。这种差异是由于加入了电荷再分布和极化等界面特异效应。此外，研究结果表明，这些化合物的能带排列在同一组卤化物中属于 I 型，由于离子半径相似，Zr- 和 Hf 基化合物的晶格常数几乎相同。这些结果为电子应用中异质结构的设计提供了宝贵的见解，并凸显了 Cs2(Ti、Zr、Hf)X6 化合物作为太阳能电池、发光二极管和光检测器的高效材料的潜力。

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引用次数: 0

WC electron microscopy image segmentation based on improved watershed and Hu-moment edge matching algorithms 基于改进的分水岭和 Hu-moment 边缘匹配算法的 WC 电子显微镜图像分割技术

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-03 DOI: 10.1016/j.commatsci.2024.113401

Yixuan Zhong , Yi Liu , Kai Liu , Teng Zhan , Shuli Liu , Yunlong Liang , Yuliang Hu , Mingfu Li , Gaopan Lei , Shiyu Zhou , Jingang Liu

The particle size distribution of WC powder particles has a great influence on material properties. However, the traditional manual particle size analysis methods are both time-consuming and inaccurate, and the commonly used particle size detection methods belong to statistical indexes, which cannot reflect the real particle size. To address the above problems, this paper proposes an image segmentation method based on the improved watershed algorithm and the Hu-moment edge matching algorithm, which can realize accurate segmentation and particle size analysis of adherent particles in WC electron microscope images. First, an improved bilateral filtering and Otsu image coarse segmentation method is proposed to extract the target region of particles; then, an improved watershed algorithm based on the multi-threshold H-maxima transform is proposed to realize the segmentation of adherent particles; and a region merging correction based on the Hu-moment edge matching algorithm is proposed to avoid over-segmentation. We compare and analyze the performance of this method with manual segmentation and some other common segmentation methods. The experimental results show that the standard deviations of the particle sizes obtained by the method proposed in this paper are less than 3%, and the segmentation accuracy is greatly improved compared with other segmentation algorithms.

WC 粉末颗粒的粒度分布对材料性能有很大影响。然而，传统的人工粒度分析方法既费时又不准确，而且常用的粒度检测方法属于统计指标，不能反映真实粒度。针对上述问题，本文提出了一种基于改进分水岭算法和Hu-moment边缘匹配算法的图像分割方法，可实现对WC电子显微镜图像中附着颗粒的精确分割和粒度分析。首先，提出了一种改进的双边滤波和大津图像粗分割方法来提取颗粒的目标区域；然后，提出了一种基于多阈值H-最大值变换的改进分水岭算法来实现附着颗粒的分割；并提出了一种基于Hu-moment边缘匹配算法的区域合并校正来避免过度分割。我们比较分析了该方法与人工分割以及其他一些常见分割方法的性能。实验结果表明，本文提出的方法得到的颗粒大小的标准偏差小于 3%，与其他分割算法相比，分割精度大大提高。

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引用次数: 0

Molecular simulation-based developer screening for molecular glass photoresists 基于分子模拟的分子玻璃光刻胶显影剂筛选

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-03 DOI: 10.1016/j.commatsci.2024.113429

Peng Lian , Rongrong Peng , Tianjun Yu , Guoqiang Yang , Jinping Chen , Yi Li , Yi Zeng

Screening of photoresist developers is critical for high-resolution lithography processes. Efficient estimation for photoresist solubility to facilitate the process of developer screening is of both fundamental and practical importance. In this study, we proposed a solubility prediction and developer screening approach for the molecular glass photoresists based on the molecular simulation calculation of two-component solubility parameters. The values of the two-component solubility parameters (δ_{Lennard-Jones} and δ_Coulomb) for 60 solvents were calculated, and their correlation with experimental Hansen solubility parameters was investigated. Meanwhile, the parameters calculation methods of binary mixed solvents with different polarities were systematically investigated. Then, the accuracy of solubility prediction was verified by dissolution experiments and Hansen solubility parameters, revealing that the two-component solubility parameters could reasonably reflect the solubilities of neutral and ionic molecular glass photoresists in most solvents. Furthermore, developer screening schemes using both pure and mixed solvents were investigated based on the two-component solubility parameters, which was further confirmed by the practical lithography experiments. The current method provides a viable approach for characterizing the photoresist solubility and screening appropriate developers, which is beneficial for accelerating the development of photoresist materials.

光刻胶显影剂的筛选对于高分辨率光刻工艺至关重要。有效估算光刻胶的溶解度以促进显影剂筛选过程具有基础性和实用性的重要意义。在本研究中，我们提出了一种基于分子模拟计算双组分溶解度参数的分子玻璃光刻胶溶解度预测和显影剂筛选方法。计算了 60 种溶剂的双组分溶解度参数值（δLennard-Jones 和 δCoulomb），并考察了它们与实验汉森溶解度参数的相关性。同时，系统研究了不同极性的二元混合溶剂的参数计算方法。然后，通过溶解实验和汉森溶解度参数验证了溶解度预测的准确性，结果表明双组分溶解度参数可以合理地反映中性和离子型分子玻璃光刻胶在大多数溶剂中的溶解度。此外，还根据双组分溶解度参数研究了使用纯溶剂和混合溶剂的显影剂筛选方案，并通过实际光刻实验进一步证实了这一点。目前的方法为表征光刻胶的溶解度和筛选合适的显影剂提供了一种可行的方法，有利于加速光刻胶材料的开发。

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引用次数: 0

Analysis of microstructure uncertainty propagation in fibrous composites Empowered by Physics-Informed, semi-supervised machine learning 纤维复合材料微观结构不确定性传播分析借助物理信息半监督式机器学习技术

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-03 DOI: 10.1016/j.commatsci.2024.113423

Qianyu Zhou , Ryan S. Enos , Kai Zhou , Haotian Sun , Dianyun Zhang , Jiong Tang

The advancements in multi-scale computational analysis of fiber reinforced composites have led to the possibility of predicting important material properties based on their microstructure characteristics. Nevertheless, major challenges remain. The fiber distributions feature inherent randomness, which naturally leads to variations in properties such as transverse strength. This in turn undermines the significance of deterministic analysis to guide manufacturing optimization. Direct Monte Carlo simulation for uncertainty analysis is computationally insurmountable, as a single run of finite element simulation is already costly. While several surrogate modeling techniques leveraging supervised learning have been explored, it is commonly recognized that the efficacy of these surrogate models hinges upon the size of training dataset. In this research we establish a semi-supervised learning framework that can produce highly accurate emulation results with much reduced size of labeled training dataset. A random fiber packing algorithm is employed to sample the representative volume element (RVE) images that are subsequently fed to the finite element analysis to generate the ground-truth labeled data used in the training of neural network. To reduce the ground-truth labeling cost while maintaining the deep learning capacity. we employ the pseudo labeling technique where the base model is initially trained on a small set of ground truth labeled data and then used to generate credible pseudo-labels for a larger pool of unlabeled data. The model is subsequently retrained on this augmented dataset with adjusted weights and biases to reflect the varying confidence in the label sources. This framework is successfully employed in the analysis of microstructure uncertainty propagation in fibrous composites. The proposed approach efficiently leverages patterns from both unlabeled and limited labeled samples to predict transverse strength for varied RVE samples, matching the efficacy of a fully supervised model trained with 1,000 ground truth labels while simultaneously slashing labeling efforts by 72%. This framework can be extended to uncertainty propagation analysis using microstructure characteristics of other materials.

纤维增强复合材料的多尺度计算分析技术的进步，为根据其微观结构特征预测重要的材料特性提供了可能。然而，重大挑战依然存在。纤维分布具有固有的随机性，这自然会导致横向强度等性能的变化。这反过来又削弱了确定性分析对生产优化的指导意义。用于不确定性分析的直接蒙特卡罗模拟在计算上是无法克服的，因为一次有限元模拟运行的成本已经很高。虽然人们已经探索了几种利用监督学习的代用模型技术，但普遍认为这些代用模型的有效性取决于训练数据集的大小。在这项研究中，我们建立了一个半监督学习框架，它能在大大减少标注训练数据集的情况下产生高精度的仿真结果。我们采用随机纤维包装算法对代表性体元（RVE）图像进行采样，然后将这些图像输入有限元分析，生成神经网络训练中使用的地面实况标记数据。为了在保持深度学习能力的同时降低地面实况标注成本，我们采用了伪标注技术，即基础模型最初在一小部分地面实况标注数据集上进行训练，然后用于为更大的未标注数据池生成可信的伪标注。随后，在这个增强数据集上对模型进行重新训练，并调整权重和偏差，以反映标签源的不同可信度。这一框架已成功应用于纤维复合材料微观结构不确定性传播的分析。所提出的方法有效地利用了未标记样本和有限标记样本的模式来预测不同 RVE 样本的横向强度，与使用 1,000 个基本真实标签训练的完全监督模型的效果相当，同时将标记工作减少了 72%。该框架可扩展到使用其他材料的微观结构特征进行不确定性传播分析。

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引用次数: 0

Phase transformation mechanism in irradiation-induced superlattice formation 辐照诱导超晶格形成过程中的相变机制

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-03 DOI: 10.1016/j.commatsci.2024.113418

Larry K. Aagesen , Yongfeng Zhang , Chao Jiang , Jian Gan

Atomic kinetic Monte Carlo simulations were used to model void superlattice formation under irradiation in molybdenum, driven by anisotropic diffusion of self-interstitial atoms. A change in the phase transformation mechanism from nucleation and growth to spinodal decomposition occurred with increasing dose rate, with both mechanisms leading to superlattice formation. Analysis of a rate-theory based analytical model showed that an observed change in the kinetics of vacancy accumulation, the appearance of a region of positive second derivative in the plot of average vacancy concentration versus time, was caused by the onset of spinodal instability. The analytical model showed that for molybdenum and several other metals where void superlattice formation is commonly observed, the phase transformation likely occurs by nucleation and growth. However, nickel may offer the possibility of experimental observation of the transition between phase transformation mechanisms.

原子动力学蒙特卡洛模拟用于模拟钼在辐照下由自间隙原子的各向异性扩散驱动的空隙超晶格形成。随着剂量率的增加，相变机制从成核和生长转变为旋光分解，两种机制都会导致超晶格的形成。对基于速率理论的分析模型的分析表明，观察到的空位累积动力学的变化，即在平均空位浓度与时间的关系图中出现正二阶导数区域，是由开始出现的旋光不稳定性引起的。分析模型显示，对于钼和其他几种经常观察到空位超晶格形成的金属，相变很可能是通过成核和生长发生的。不过，镍可能提供了实验观察相变机制之间转变的可能性。

引用次数: 0

Construction of Al–Si interatomic potential based on Bayesian active learning 基于贝叶斯主动学习的铝硅原子间势构建

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-02 DOI: 10.1016/j.commatsci.2024.113422

Xuedong Liu , Yan Zhang , Hui Xu

Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems, and the prohibitively high computational cost associated with ab initio molecular dynamics simulations. In order to enhance simulation efficiency and accuracy of the Al–Si alloys’ microstructural evolution, this study employs a dynamic active learning technique, FLARE, to develop a non-parametric machine learning potential that combines the high accuracy of density functional theory (DFT) with the efficiency of classical molecular dynamics (MD). Without relying on extensive initial ab initio molecular dynamics data or existing databases, collection of the necessary data is progressively made during the active learning process, thereby constructing a potential capable of accurately simulating the structure and dynamics of high-temperature Al–Si alloys. By comparing with experimental measurements and ab initio molecular dynamics calculations, the high accuracy and computational efficiency of this potential is demonstrated in predicting energy, force, structure, and dynamic properties. The results provide novel theoretical insights for optimizing Al–Si alloy processing and underscore the usefulness of active learning methods in constructing high-accuracy potentials.

用于优化铝硅合金性能和加工工艺的纳米级模拟目前面临两大障碍：一是针对复杂合金体系量身定制的高质量半经验势能稀缺，二是与原子分子动力学模拟相关的计算成本过高。为了提高铝硅合金微观结构演变的模拟效率和准确性，本研究采用了一种动态主动学习技术--FLARE--来开发一种非参数机器学习势，它结合了密度泛函理论（DFT）的高准确性和经典分子动力学（MD）的高效性。在不依赖大量初始原子分子动力学数据或现有数据库的情况下，在主动学习过程中逐步收集必要的数据，从而构建出能够精确模拟高温铝硅合金结构和动力学的势。通过与实验测量和原子分子动力学计算的比较，证明了该势能在预测能量、力、结构和动态特性方面的高准确性和计算效率。研究结果为优化铝硅合金加工提供了新的理论见解，并强调了主动学习方法在构建高精度势能方面的实用性。

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引用次数: 0

The electronic and optical properties of group III-V semiconductors: Arsenides and Antimonides III-V 族半导体的电子和光学特性：砷化物和锑化物

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-01 DOI: 10.1016/j.commatsci.2024.113381

Ruixin Gong , Lianqing Zhu , Qingsong Feng , Lidan Lu , Bingfeng Liu , Yuhao Chen , Yuanbo Zhang , Shiya Zhang , Yang Chen , Zhiying Liu

Investigating the structural, electronic, and optical properties of zinc-blende III-V semiconductors, particularly arsenides, and antimonides, which are crucial for optoelectronic devices such as transistors, infrared detectors, and quantum technologies due to their wide range of direct bandgaps. In this work, we have employed a first-principles approach integrating G₀W₀ with the HSE06 hybrid functional and spin–orbit coupling (SOC) to study their fundamental properties. Traditional Density Functional Theory (DFT) methods, particularly those using Generalized Gradient Approximation (GGA) PBE functionals, tend to underestimate bandgaps, leading to discrepancies with experimental results. To address this, our study corrects the bandgap underestimation and refines the calculation of optical constants, including the dielectric function, refractive index, extinction coefficient, and absorption coefficient. Moreover, the optimized lattice constants and electronic properties derived from our computational model strongly correlate with experimental data, demonstrating the model’s reliability in predicting material properties. The findings suggest that our methods can be applied to arsenides and antimonides, offering a pathway to designing materials with optoelectronic properties involving III-V compounds and their complex heterostructures for advanced device applications.

研究锌蓝III-V族半导体（尤其是砷化物和锑化物）的结构、电子和光学特性，由于它们具有广泛的直接带隙，因此对晶体管、红外探测器和量子技术等光电设备至关重要。在这项研究中，我们采用了第一原理方法，将 G0W0 与 HSE06 混合函数和自旋轨道耦合 (SOC) 结合起来，研究它们的基本特性。传统的密度泛函理论（DFT）方法，尤其是使用广义梯度逼近（GGA）PBE 函数的方法，往往会低估带隙，从而导致与实验结果的差异。为了解决这个问题，我们的研究纠正了带隙低估，并完善了光学常数的计算，包括介电函数、折射率、消光系数和吸收系数。此外，我们的计算模型得出的优化晶格常数和电子特性与实验数据密切相关，证明了模型在预测材料特性方面的可靠性。研究结果表明，我们的方法可应用于砷化物和锑化物，为设计具有光电特性的材料提供了一条途径，这些材料涉及 III-V 化合物及其复杂的异质结构，可用于先进的设备应用。

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引用次数: 0

Ab initio molecular dynamics simulations on the combustion mechanism of Al/Fe2O3 nanothermite at various temperatures 不同温度下 Al/Fe2O3 纳米热敏电阻燃烧机理的 Ab initio 分子动力学模拟

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-01 DOI: 10.1016/j.commatsci.2024.113427

Chuang Xue , Pin Gao , Guixiang Wang , Xuedong Gong

Acquiring a comprehensive understanding of the combustion mechanism of nanothermites is crucial for elucidating phenomena and enhancing performance. The ab initio molecular dynamics method was employed to investigate the reaction process of the Al/Fe₂O₃ nanothermite at various temperatures. The complex combustion behavior and reaction mechanism were qualitatively described in terms of dynamic morphologies, potential energy, atomic density distribution, etc. Results suggest the initial reaction of Al/Fe₂O₃ is initiated by the migration of interfacial O atoms and the dissociation of Fe-O bonds, subsequently leading to the formation of alumina at the interface, which impedes the further progression of the thermite reaction. The increase in temperature enhances atomic diffusion and provides sufficient energy for the reaction. The interfacial metal Al layer undergoes melting and diffuses into the iron oxide layer, while vacancies generated during the reaction process sustain the continuous migration of internal oxygen atoms. At 2000 K and 3200 K, the initial structures completely collapse, facilitating the inward propagation of the thermite reaction, which subsequently results in the formation of alumina, iron clusters, and intermetallic compounds (AlFe, AlFe₃, and Al₆Fe). These findings offer significant insights into the combustion reaction mechanisms of nanothermites.

全面了解纳米热物的燃烧机理对于阐明现象和提高性能至关重要。本文采用ab initio分子动力学方法研究了Al/Fe2O3纳米热物在不同温度下的反应过程。从动态形貌、势能、原子密度分布等方面对复杂的燃烧行为和反应机理进行了定性描述。结果表明，Al/Fe2O3 的初始反应是由界面 O 原子的迁移和 Fe-O 键的解离引发的，随后在界面上形成氧化铝，阻碍了热敏反应的进一步进行。温度的升高加强了原子扩散，为反应提供了足够的能量。界面金属铝层发生熔化并扩散到氧化铁层中，而反应过程中产生的空位则维持着内部氧原子的不断迁移。在 2000 K 和 3200 K 时，初始结构完全坍塌，促进了热敏反应的向内扩展，随后形成氧化铝、铁簇和金属间化合物（AlFe、AlFe3 和 Al6Fe）。这些发现为了解纳米热敏物质的燃烧反应机制提供了重要启示。

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引用次数: 0

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification 用于凝固过程中金属晶粒生长相场建模的深度算子网络代用工具

IF 3.1 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2024-10-01 DOI: 10.1016/j.commatsci.2024.113417

Danielle Ciesielski, Yulan Li, Shenyang Hu, Ethan King, Jordan Corbey, Panos Stinis

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the Allen–Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434

g

uranium charge from a maximum temperature of 1400 °C at an average rate of 30 °

\frac{C}{min}

, traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal’s microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. These relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

我们构建了一个深度算子网络（DeepONet），可生成相场模型模拟的精确表征，用于分析从熔体中生长出来的二维金属晶粒形态。这些表示可作为分辨率较低、计算效率较高的替身，用于快速探索决定相场模型模拟的艾伦-卡恩方程的参数空间解。相场模型的实验目标是一个铀铸造系统，以平均每分钟 30°C 的速度将 434 克铀装料从 1400°C 的最高温度冷却下来，穿越纯金属的结晶相。实验参数为相场模型提供了信息，而相场模型的高分辨率计算模型解则用于在给定参数空间内训练 DeepONet，目的是开发一种更快、更有效的方法，在不同的潜在实验值下预测凝固金属的微观结构。最终的 DeepONet 可生成高精度、低分辨率的预测结果，所有时间步长的累计相对近似误差小于 0.5%，同时确保解决方案保持在物理可行范围内。这些相对误差值与其他最先进的微结构演化 DeepONet 模型相当，同时大大减少了所需的训练数据量。与标准 DeepONet 模型相比，同时训练卷积神经网络和 DeepONet、在复杂金属晶粒边界强制执行现实值，以及将边界条件数学化编码到 DeepONet 结构中，提高了预测精度和计算效率。

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引用次数: 0