Materials Genome Engineering Advances最新文献_第3页

Multiobjective optimization of dielectric, thermal, and mechanical properties of inorganic glasses utilizing explainable machine learning and genetic algorithm 利用可解释的机器学习和遗传算法对无机玻璃的介电、热和机械性能进行多目标优化

Materials Genome Engineering Advances Pub Date : 2025-03-24 DOI: 10.1002/mgea.70005

Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu

{"title":"Multiobjective optimization of dielectric, thermal, and mechanical properties of inorganic glasses utilizing explainable machine learning and genetic algorithm","authors":"Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu","doi":"10.1002/mgea.70005","DOIUrl":"https://doi.org/10.1002/mgea.70005","url":null,"abstract":"To meet the demands of advanced electronic devices, inorganic glasses are required to have comprehensive dielectric, thermal, and mechanical properties. However, the complex composition–property relationship and vast compositional diversity hinder optimization. This study developed machine learning models to predict permittivity, dielectric loss, thermal conductivity, coefficient of thermal expansion, and Young’s modulus based on the composition features of inorganic glasses. The optimal models achieve R2 values of 0.9614, 0.7411, 0.9454, 0.9684, and 0.8164, respectively. By integrating domain knowledge with model-agnostic interpretation methods, feature contributions and interactions were analyzed. The mixed alkali effect is crucial for property regulation, especially Na-K for dielectric loss and Na-Li for thermal conductivity. Boron anomaly shifts the high-λ region to a balanced composition of alkali metals with rising B%. The multiobjective optimization of properties was realized using a genetic algorithm framework. After 23 iterations, the optimal material in the MgO-Al2O3-B2O3-SiO2 system exhibits εr = 4.78, tanδ = 0.00063, λ = 2.59 W/(m·K), α = 50.27×10−7K−1, and E = 82.41 GPa, outperforming all materials in the dataset. The computational effort was reduced to 1/19 of that required using exhaustive search methods. This study provides a model interpretation framework and an effective multiobjective optimization strategy for glass design.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514799","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

Enhancing named entity recognition with a novel BERT-BiLSTM-CRF-RC joint training model for biomedical materials database 基于BERT-BiLSTM-CRF-RC联合训练模型的生物医学材料数据库命名实体识别

Materials Genome Engineering Advances Pub Date : 2025-03-16 DOI: 10.1002/mgea.70001

Mufei Li, Yan Zhuang, Ke Chen, Lin Han, Xiangfeng Li, Yongtao wei, Xiangdong Zhu, Mingli Yang, Guangfu Yin, Jiangli Lin, Xingdong Zhang

{"title":"Enhancing named entity recognition with a novel BERT-BiLSTM-CRF-RC joint training model for biomedical materials database","authors":"Mufei Li, Yan Zhuang, Ke Chen, Lin Han, Xiangfeng Li, Yongtao wei, Xiangdong Zhu, Mingli Yang, Guangfu Yin, Jiangli Lin, Xingdong Zhang","doi":"10.1002/mgea.70001","DOIUrl":"https://doi.org/10.1002/mgea.70001","url":null,"abstract":"In this study, we propose a novel joint training model for named entity recognition (NER) that combines BERT, BiLSTM, CRF, and a reading comprehension (RC) mechanism. Traditional BERT-BiLSTM-CRF models often struggle with inaccurate boundary detection and excessive fragmentation of named entities due to their lack of specialized vocabulary. Our model addresses these issues by integrating an RC mechanism, which helps refine fragmented results by enabling the model to more precisely identify entity boundaries without relying on an expert-annotated dictionary. Additionally, segmentation issues are further mitigated through a segmented combined voting- and positive-sample-coverage technique. We applied this model to develop a database for mesoporous bioactive glass (MBG). Furthermore, a classifier was developed to automatically detect the presence of pertinent information within paragraphs. For this study, 200 articles were searched using MBG-related keywords, and the data were split into a training set and a test set in a 9:1 ratio. A total of 492 paragraphs were automatically extracted for training, and 50 paragraphs were extracted for testing the model. The results demonstrate that our joint training model achieves an accuracy of 92.8% in named entity recognition, which is 4.3% higher than the 88.5% accuracy of the traditional BERT-BiLSTM-CRF model.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717162","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

Advances in high-throughput experiments of polymer crystallization for developing polymer processing 聚合物结晶的高通量实验研究进展

Materials Genome Engineering Advances Pub Date : 2025-03-13 DOI: 10.1002/mgea.70003

Bao Deng, Jinyong Wu, Hao Lin, Ling Xu, Ganji Zhong, Jun Lei, Ludwig Cardon, Jiazhuang Xu, Zhongming Li

{"title":"Advances in high-throughput experiments of polymer crystallization for developing polymer processing","authors":"Bao Deng, Jinyong Wu, Hao Lin, Ling Xu, Ganji Zhong, Jun Lei, Ludwig Cardon, Jiazhuang Xu, Zhongming Li","doi":"10.1002/mgea.70003","DOIUrl":"https://doi.org/10.1002/mgea.70003","url":null,"abstract":"Polymer crystallization, an everlasting subject in polymeric materials, holds great significance not only as a fundamental theoretical issue but also as a pivotal basis for directing polymer processing. Given its multistep, rapid, and thermodynamic nature, tracing and comprehending polymer crystallization pose a formidable challenge, particularly when it encounters practical processing scenarios that involve complex coupled fields (such as temperature, flow, and pressure). The advent of high-time and spatially resolved experiments paves the way for in situ investigations of polymer crystallization. In this review, we delve into the strides in studying polymer crystallization under the effects of coupled external fields via state-of-the-art high-throughput experiments. We highlight the intricate setup of these high-throughput experimental devices, spanning from the laboratory and pilot levels to the industrial level. The individual and combined effects of external fields on polymer crystallization are discussed. By breaking away from the conventional “black box” research approach, special interest is paid to the in situ crystalline behavior of polymers during realistic processing. Finally, we underscore the advancements in polymer crystallization via high-throughput experiments and outline its promising development.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717363","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 calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi-principal element alloys 结合堆垛集成机器学习的高通量计算方法预测难熔多主元合金的弹性性能

Materials Genome Engineering Advances Pub Date : 2025-03-12 DOI: 10.1002/mgea.70004

Chengchen Jin, Kai Xiong, Congtao Luo, Hui Fang, Chaoguang Pu, Hua Dai, Aimin Zhang, Shunmeng Zhang, Yingwu Wang

{"title":"High-throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi-principal element alloys","authors":"Chengchen Jin, Kai Xiong, Congtao Luo, Hui Fang, Chaoguang Pu, Hua Dai, Aimin Zhang, Shunmeng Zhang, Yingwu Wang","doi":"10.1002/mgea.70004","DOIUrl":"https://doi.org/10.1002/mgea.70004","url":null,"abstract":"The traditional trial-and-error method for designing refractory multi-principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data-driven material design based on machine learning (ML) has emerged as a critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High-throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data-driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti-V-Nb-Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost-effective, efficient, and precise alloy design strategy, advancing RMPEAs development.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146247","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

Local large language model-assisted literature mining for on-surface reactions 局部大语言模型辅助表面反应文献挖掘

Materials Genome Engineering Advances Pub Date : 2025-03-12 DOI: 10.1002/mgea.88

Juan Xiang, Yizhang Li, Xinyi Zhang, Yu He, Qiang Sun

{"title":"Local large language model-assisted literature mining for on-surface reactions","authors":"Juan Xiang, Yizhang Li, Xinyi Zhang, Yu He, Qiang Sun","doi":"10.1002/mgea.88","DOIUrl":"https://doi.org/10.1002/mgea.88","url":null,"abstract":"Large language models (LLMs) excel at extracting information from literatures. However, deploying LLMs necessitates substantial computational resources, and security concerns with online LLMs pose a challenge to their wider applications. Herein, we introduce a method for extracting scientific data from unstructured texts using a local LLM, exemplifying its applications to scientific literatures on the topic of on-surface reactions. By combining prompt engineering and multi-step text preprocessing, we show that the local LLM can effectively extract scientific information, achieving a recall rate of 91% and a precision rate of 70%. Moreover, despite significant differences in model parameter size, the performance of the local LLM is comparable to that of GPT-3.5 turbo (81% recall, 84% precision) and GPT-4o (85% recall, 87% precision). The simplicity, versatility, reduced computational requirements, and enhanced privacy of the local LLM makes it highly promising for data mining, with the potential to accelerate the application and development of LLMs across various fields.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.88","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717403","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 multi-objective feature optimization strategy for developing high-entropy alloys with optimal strength and ductility 高熵合金强度和延展性的多目标特征优化策略

Materials Genome Engineering Advances Pub Date : 2025-03-05 DOI: 10.1002/mgea.70000

Yan Zhang, Shewei Xin, Wei Zhou, Xiao Wang, Yangyang Xu, Yanjing Su

{"title":"A multi-objective feature optimization strategy for developing high-entropy alloys with optimal strength and ductility","authors":"Yan Zhang, Shewei Xin, Wei Zhou, Xiao Wang, Yangyang Xu, Yanjing Su","doi":"10.1002/mgea.70000","DOIUrl":"https://doi.org/10.1002/mgea.70000","url":null,"abstract":"Selecting appropriate material features is essential for effective data-driven materials design. Here, we propose a multi-objective feature optimization strategy that identifies feature subsets to improve both prediction accuracy and active learning efficiency for iterative experimentation. Our approach integrates an evolutionary genetic algorithm to explore an expanded feature space, encompassing both traditional feature pools and a continuous numerical representation of elements rather than relying solely on discrete values. We demonstrate this strategy by identifying high-entropy alloys (HEAs) with optimal strength and ductility. Results show that the optimized feature subsets reduce prediction errors by 20% for strength and 11% for ductility. Additionally, within fewer than three feedback iterations, HEAs with outstanding combinations of yield strength and ductility are identified, highlighting the high efficiency of this approach. This multi-objective feature optimization strategy is adaptable to other material systems, offering a pathway to improve machine learning performance and accelerate materials discovery.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717088","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 assessment of Nb, Co, Ti, and Al effects on microstructure and mechanical properties in wrought nickel-based superalloys Nb, Co， Ti和Al对变形镍基高温合金组织和力学性能影响的高通量评估

Materials Genome Engineering Advances Pub Date : 2025-02-20 DOI: 10.1002/mgea.70002

Haoyan Meng, Jin Huang, Tianhao Zhao, Xiaoyu Zhang, Yang Tong, Jinglong Qu, Weidong Li, Liang Jiang, Fanchao Meng, Shuying Chen

{"title":"High-throughput assessment of Nb, Co, Ti, and Al effects on microstructure and mechanical properties in wrought nickel-based superalloys","authors":"Haoyan Meng, Jin Huang, Tianhao Zhao, Xiaoyu Zhang, Yang Tong, Jinglong Qu, Weidong Li, Liang Jiang, Fanchao Meng, Shuying Chen","doi":"10.1002/mgea.70002","DOIUrl":"https://doi.org/10.1002/mgea.70002","url":null,"abstract":"The influence of Nb, Co, Ti, and Al on the microstructural evolution and mechanical properties of GH4061 superalloy was simultaneously investigated using diffusion multiple techniques combined with double aging heat treatment. Co exhibited the highest penetration depth with monotonic concentration decrease, significantly inhibiting the formation of needle-like δ phase and reduced the precipitation size of the γ′ phase. Nb diffusion formed distinct layers with clear interfaces, promoting substantial γ′′ phase precipitation, while simultaneously enhancing hardness through solid solution strengthening. Ti and Al diffusion generated substantial diffusion layers containing Ti-Ni enriched needle-like η phases and enhanced γ′ phase growth, characterized by increased size and volume fraction compared to the base alloy. In contrast to the base alloy, this diffusion layer was devoid of phases enriched with Nb. Mechanical property analysis demonstrated that high concentrations of Nb, Al, and Ti enhanced alloy hardness through various strengthening mechanisms, whereas increased Co content diminished the size of the γ′ phase at the interface, resulting in reduced hardness. This high-throughput experimental approach significantly reduced experimental workload while enabling detailed analysis of varying element concentrations on microstructure and properties.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146271","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 overview of high-throughput synthesis for advanced high-entropy alloys 先进高熵合金的高通量合成综述

Materials Genome Engineering Advances Pub Date : 2025-02-20 DOI: 10.1002/mgea.87

Tong Xie, Weidong Li, Gihan Velisa, Shuying Chen, Fanchao Meng, Peter K. Liaw, Yang Tong

引用次数: 0

A knowledge graph attention network for the cold-start problem in intelligent manufacturing: Interpretability and accuracy improvement 智能制造冷启动问题的知识图谱关注网络：可解释性和准确性的提高

Materials Genome Engineering Advances Pub Date : 2025-02-13 DOI: 10.1002/mgea.85

Ziye Zhou, Yuqi Zhang, Shuize Wang, David San Martin, Yongqian Liu, Yang Liu, Chenchong Wang, Wei Xu

{"title":"A knowledge graph attention network for the cold-start problem in intelligent manufacturing: Interpretability and accuracy improvement","authors":"Ziye Zhou, Yuqi Zhang, Shuize Wang, David San Martin, Yongqian Liu, Yang Liu, Chenchong Wang, Wei Xu","doi":"10.1002/mgea.85","DOIUrl":"https://doi.org/10.1002/mgea.85","url":null,"abstract":"In the rolling production of steel, predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories. This scenario poses a significant hurdle for machine learning models, leading to what is commonly known as the “cold-start problem”. To address this issue, we propose a knowledge graph attention neural network for steel manufacturing (SteelKGAT). By leveraging expert knowledge and a multi-head attention mechanism, SteelKGAT aims to enhance prediction accuracy. Our experimental results demonstrate that the SteelKGAT model outperforms existing methods when generalizing to previously unseen products. Only the SteelKGAT model accurately captures the feature trend, thereby offering correct guidance in product tuning, which is of practical significance for new product development (NPD). Additionally, we employ the Integrated Gradients (IG) method to shed light on the model's predictions, revealing the relative importance of each feature within the knowledge graph. Notably, this work represents the first application of knowledge graph attention neural networks to address the cold-start problem in steel rolling production. By combining domain expertise and interpretable predictions, our knowledge-informed SteelKGAT model provides accurate insights into the mechanical properties of products even in cold-start scenarios.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.85","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514872","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

Thermodynamics and kinetics of isothermal precipitation in magnesium alloys 镁合金等温析出的热力学和动力学

Materials Genome Engineering Advances Pub Date : 2025-02-09 DOI: 10.1002/mgea.86

Hongcan Chen, Jingli Sun, Shenglan Yang, Yu Zhang, Kai Tang, Chuan Zhang, Yangfan Lu, Qun Luo, Qian Li

{"title":"Thermodynamics and kinetics of isothermal precipitation in magnesium alloys","authors":"Hongcan Chen, Jingli Sun, Shenglan Yang, Yu Zhang, Kai Tang, Chuan Zhang, Yangfan Lu, Qun Luo, Qian Li","doi":"10.1002/mgea.86","DOIUrl":"https://doi.org/10.1002/mgea.86","url":null,"abstract":"As the lightest structural metal materials, Mg alloys are promising for wider applications but are limited by low strength and poor corrosion resistance. Precipitation is an effective way to improve the strength and other performance of Mg alloys. Facing the extremely complex precipitation process, the crystal structures of precipitates, precipitation sequence, and precipitation thermodynamic and kinetics behaviors have stimulated extensive research interests. Precipitation kinetics, which connects composition, aging processes, and precipitate microstructure, is pivotal in determining the performance of age-hardening Mg alloys. Despite numerous studies on this topic, a comprehensive review remains absent. This work aims to bridge that gap by analyzing precipitation from thermodynamic and kinetic perspectives. Thermodynamically, the stability of precipitates, nucleation driving forces, and resistances of precipitation are discussed. Kinetically, the various kinetic theories including semi-empirical models, mean-field models, phase-field model, and atomistic approaches and their applications in Mg alloys are systematically summarized. Among these, mean-field models emerge as particularly promising for accurately predicting precipitation processes. Finally, the framework for property prediction based on precipitation kinetics is introduced to illustrating the role of integrated computational materials engineering (ICME) in designing advanced Mg alloys.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.86","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717361","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