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Machine Learning-science And Technology

Machine Learning-science And Technology杂志,由IOP PUBLISHING LTD出版,于2020年创刊,Quarterly,出版语言English,ISSN:2632-2153,E-ISSN:2632-2153。

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Machine Learning-science And Technology
Machine Learning-science And Technology
Machine Learning-science And Technology
SCI SCIE

杂志介绍

JCR分区
Q1
中科院分区
2区
影响因子
4.2
CiteScore
6.7
期刊收录
SCI、SCIE

Machine Learning-science And Technology(中文译名:《机器学习-科学与技术》),ISSN:2632-2153,EISSN:2632-2153,是IOP PUBLISHING LTD出版的国际性学术期刊,创刊于2020年,以Quarterly形式稳定发行,2026年总发文量约321篇。该刊采用OA开放访问,学科归属为Multiple。该刊是物理与天体物理、计算机人工智能领域的国际权威刊物,已被SCI(科学引文索引)、SCIE(科学引文索引扩展版)等国际主流学术数据库收录。2026年期刊影响因子达4.2,2025年期刊CiteScore为6.7,2025年期刊自引率约7.1%,在中科院期刊分区体系中位列物理与天体物理大类2区,在所属学科领域具备高学术影响力与国际认可度。Machine Learning-science And Technology长期聚焦物理与天体物理、计算机人工智能及相关产业的技术应用前沿,平均审稿周期13 Weeks,审稿流程高效稳定。在稿件录用评判中,该刊将创新性与前沿性作为核心遴选标准,重点收录能够对物理与天体物理、计算机人工智能领域的落地与发展产生实质性推动价值的研究成果。

期刊评价

名词解释:

影响因子(Impact Factor, IF):指该期刊前两年发表的文章,在第三年的平均被引用次数。它反映了期刊的近期平均影响力和热度。

中科院分区:中科院分区表是国内主流的学术期刊分级评价工具,核心意义是建立跨学科可比的统一评价标尺,为职称评审、学位授予、科研立项等科研管理工作提供标准化量化依据,同时帮助科研人员筛选优质期刊、规避学术风险,适配国内本土化的科研评价需求。

期刊分区表

《新锐期刊分区表》(2026年3月发布)

大类学科 小类学科 Top期刊 综述期刊
物理与天体物理
2区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机:跨学科应用
3区 3区

期刊分区表(2025年3月升级版)

大类学科 小类学科 Top期刊 综述期刊
物理与天体物理
2区
MULTIDISCIPLINARY SCIENCES 综合性期刊 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机:跨学科应用
2区 3区 3区

期刊分区表(2023年12月升级版)

大类学科 小类学科 Top期刊 综述期刊
物理与天体物理
2区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 MULTIDISCIPLINARY SCIENCES 综合性期刊 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机:跨学科应用
2区 2区 3区

JCR分区

2025-2026年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q2 87 / 210

58.8

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS SCIE Q2 70 / 185

62.4

学科:MULTIDISCIPLINARY SCIENCES SCIE Q1 26 / 140

81.8

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q2 75 / 210

64.52

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS SCIE Q2 78 / 185

58.11

学科:MULTIDISCIPLINARY SCIENCES SCIE Q2 37 / 140

73.93

2023-2024年最新版

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 36 / 197

82

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS SCIE Q1 23 / 169

86.7

学科:MULTIDISCIPLINARY SCIENCES SCIE Q1 15 / 134

89.2

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 43 / 198

78.54

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS SCIE Q1 40 / 169

76.63

学科:MULTIDISCIPLINARY SCIENCES SCIE Q1 21 / 135

84.81


中国学者近期发文

1
sFWI: physics-informed score-based generative modeling for robust full waveform inversio

Author:Gai, Zicheng; Wang, Yanfei

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 2, pp. -. DOI: 10.1088/2632-2153/ae55fb

2
Enhancing transfer learning in angle-resolved photoemission spectroscopy (ARPES) with spatially-aware representations via graph convolutio

Author:Sugiarto, Hendrik Santoso; Ekahana, Sandy Adhitia; Wijaya, Bryan Christofer; Winata, Genta Indra; Soh, Y.; Aeppli, G

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 2, pp. -. DOI: 10.1088/2632-2153/ae3fe7

3
UPD-Diff: a unified precipitation downscaling method based on multi-stream elucidating diffusion mode

Author:Chen, Jiahan; Kong, Lingzhi; Cao, Yi; Zhong, Yuanzhi; Tang, Zhenfei; Li, Xinting; Yuan, Chengsheng

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 2, pp. -. DOI: 10.1088/2632-2153/ae4da6

4
Interpretable feature interaction via statistical self-supervised learning on tabular dat

Author:Zhang, Xiaochen; Xiong, Haoyi

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae3104

5
Machine learning-driven classification of natural disasters via parallel confidence fusio

Author:Li, Hongru; Li, Xihai; Liu, Jihao; Wang, Yiting; Liu, Zhigang; Zeng, Xiaoniu

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae3c58

6
Data-driven and self-supervised spectral operator learning methods for heat conduction equation with variable source function

Author:Wu, Xiangyao; Liu, Ziyuan; Bai, Ruijie; Wu, Yuhang; Qian, Xu

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae3e38

7
Error estimates for a physics-informed neural network in solving KdV equation

Author:Guo, Jia; Liu, Ziyuan; Hou, Chenping

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae3c59

8
High-resolution regional SST AI downscaling based on multi-mode inputs from nested ROMS simulation

Author:Chen, Xiaodan; Zheng, Fei; Xia, Jiangjiang; Zhu, Jiang; Shu, Yeqiang; Liu, Danian

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae3054

9
Hermite neural operator for solving partial differential equations on unbounded domains

Author:Bai, Ruijie; Liu, Ziyuan; Wu, Xiangyao; Wu, Yuhang; Qian, Xu

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae2bbc

10
GNADET: A geospatial neural advection-diffusion equation framework with graph transformer for surface temperature forecastin

Author:Lu, Yucong; Mo, Xinyue; Li, Huan

Journal: MACHINE LEARNING-SCIENCE AND TECHNOLOGY. 2026; Vol. 7, Issue 1, pp. -. DOI: 10.1088/2632-2153/ae2f8b


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Machine Learning-science And Technology

国际简称:MACH LEARN-SCI TECHN参考译名:机器学习-科学与技术

年发文量:321 CiteScore:6.7 是否预警:否 Gold OA文章占比:100.00% 研究类文章占比:97.82%

杂志社联系方式:IOP PUBLISHING LTD, TEMPLE CIRCUS, TEMPLE WAY, BRISTOL, ENGLAND, BS1 6BE

Machine Learning-science And Technology