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Journal of Materials Informatics 封面图

Journal of Materials Informatics

IF5.6
论文数36
被引数349
期刊论文 36
发表时间
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Exploring the Pareto front of strength-conductivity trade-off: interpretable machine learning for property prediction and composition design in high-strength high-conductivity Cu alloys探索强度-导电性权衡的帕累托前沿：用于高强高导铜合金性能预测和成分设计的可解释机器学习
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deleteWu, Chenggang; Liao, Mingqing; Zhou, Xinyi; Chen, Jinwei; Qian, Xiangrui; Wang, Yuehua; Zhou, Fei; Wang, Fengjiang
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Advances in graph neural networks for alloy design and properties predictions: a review图神经网络在合金设计与性能预测方面的进展：综述
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deleteZhao, Zhede; Hu, Tao; Bi, Shuyu; Guan, Dongwei; Xu, Songzhe; Chen, Chaoyue; Xuan, Weidong; Ren, Zhongming
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Multiscale simulations of Ge-Sb-Se-Te phase-change alloys for photonic memory applications用于光子存储应用的Ge-Sb-Se-Te相变合金的多尺度模拟
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deleteLi, Huiyu; Zhang, Hanyi; Ma, Wanting; Gao, Yuan; Zhou, Wen; Zhang, Wei
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Agentic material science智能材料科学
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deleteLi, Chengbo; Ran, Nian; Liu, Jianjun
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A data-driven comparative study of thermomechanical properties in rare-earth zirconate and tantalate oxides for thermal barrier coatings基于数据驱动的稀土锆酸盐和钽酸盐氧化物热障涂层的热机械性能比较研究
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deleteZhang, Ying; Wang, William Yi; Ren, Ke; Wang, Zhou; Gao, Xingyu; Wang, Yiguang; Zhang, Keke; Song, Haifeng; Liang, Xiubing; Li, Jinshan
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Machine learning-driven new paradigm for Co-based superalloys机器学习驱动的Co基超合金新范式
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deleteLuo, Jiahao; Liu, Xili; Ma, Qingshuang; Pei, Chenghao; Yao, Huiwen; Xiong, Jie; Gao, Qiuzhi
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Multi-fidelity learning in materials informatics: methodologies, applications, and outlook多尺度学习在材料信息学中的方法论、应用与展望
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deleteWang, Bo; Xu, Yangyang; Zhou, Yumei; Xue, Dezhen
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Accelerating materials discovery via AI-Agent integration of large language models and simulation tools通过人工智能代理整合大型语言模型和模拟工具加速材料发现
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deleteWang, Xian; Zeng, Qun; Xu, Dong-Hui; Zhang, Li; Jiang, Gang; Yang, Mingli
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Multi-objective optimization of fiber laser welding parameters for 316L stainless steel316L不锈钢光纤激光焊接参数的多目标优化
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deleteLi, Shuai; Wu, Jianzhao; Li, Chunjin; Zhang, Chaoyong; Xie, Yang
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Quantification and rating of casting blowhole defects using an instance segmentation algorithm使用实例分割算法对铸造气孔缺陷进行定量化与评级
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deleteDuan, Zhiqiang; Pan, Yue; Hou, Hua; Zhao, Yuhong
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Uncertainty estimation affects predictor selection and its calibration improves materials optimization不确定性估计影响预测器选择，其校准改进材料优化
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deleteJiang, Yuan; Li, Jinshan; Yuan, Tinghuan; Wang, Jun; Tang, Bin; Mao, Xinping; Li, Gang; Yuan, Ruihao
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Machine learning-accelerated transition state prediction for strain-engineered high-entropy alloy catalysts机器学习加速的应变工程高熵合金催化剂过渡态预测
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deleteWang, Lei; Zhou, Feiyu; Ying, Penghua; Chen, Yan; Liu, Yilun
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Advancing carbon dots research with machine learning: a comprehensive review利用机器学习推进碳点研究：综述
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deleteRen, Yuze; Yan, Xuetao; Fang, Rongwei; Deng, Haibo; Chen, Yingying; Li, Zhenzhen; Feng, Lingyan; Qu, Xiaogang
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Computer vision for efficient object detection and segmentation in molecular image analysis计算机视觉在分子图像分析中高效的目标检测与分割
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deleteYuan, Shaoxuan; Zhu, Zhiwen; Lu, Jiayi; Cai, Liangliang; Sun, Qiang
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The synergy of geometric tolerance factor and machine learning in discovering stable materials几何容差因子与机器学习在稳定材料发现中的协同作用
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deleteWang, Zhilong; You, Fengqi
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Enhanced multi-tuple extraction for materials: integrating pointer networks and augmented attention材料多元组提取的增强：整合指针网络和增强注意力
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deleteHei, Mengzhe; Zhang, Zhouran; Liu, Qingbao; Pan, Yan; Zhao, Xiang; Peng, Yongqian; Ye, Yicong; Zhang, Xin; Bai, Shuxin
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Physically synthesized data for deep learning-based visual scratch inspection of aerospace alloys with complex geometries基于深度学习的航空航天合金复杂几何结构视觉划痕检测的物理合成数据
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deleteWang, Yuanbin; Bai, Yupeng; Wang, Peng; Wang, Wenhu; Zhu, Mingzhu; Luo, Ming
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Stacked machine learning for accurate and interpretable prediction of MXenes' work function堆叠机器学习方法用于准确且可解释的MXenes功函数预测
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deleteShang, Lijun; Yang, Yongli; Yu, Yadong; Xiang, Pan; Ma, Li; Guo, Zhonglu; Dai, Mengyan
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Ultralow thermal conductivity via weak interactions in PbSe/PbTe monolayer heterostructure for thermoelectric design通过PbSe/PbTe单层异质结构中的弱相互作用实现超低热导率，用于热电器件设计
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deleteTan, Ruihao; Zhang, Kaiwang; Fang, Yue-Wen
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Unlocking the future of materials science: key insights from the DCTMD workshop解锁材料科学的未来：DCTMD 工作坊的关键见解
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deleteKobayashi, Rika; Amos, Roger D.; Crawford, T. Daniel; Hao, Hongxia; Liu, Yi; Lookman, Turab; Ramprasad, Rampi; Scheffler, Matthias; Wang, Hong; Zhang, Tong-Yi
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