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首页 > SCI期刊 > 物理与天体物理期刊 > Machine Learning-science And Technology(非官网)

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同大类学科其它级别期刊：

中科院 1区期刊 JCR Q1 期刊中科院 2区期刊 JCR Q2 期刊中科院 3区期刊 JCR Q3 期刊中科院 4区期刊 JCR Q4 期刊

Machine Learning-science And Technology

SCI SCIE

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

主要研究方向：Multiple 非预警期刊审稿周期：13 Weeks

《机器学习-科学与技术》(Machine Learning-science And Technology)是一本由IOP PUBLISHING LTD出版的以Multiple为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本开放期刊，近三年没有被列入预警名单。该期刊享有很高的科学声誉，影响因子不断增加，发行量也同样高。

2区中科院分区
Q1 JCR分区
321 年发文量
4.2 IF影响因子
开放是否OA
2020 创刊年份
Quarterly 出版周期
English 出版语言

Machine Learning: Science and Technology™ is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories:

i) advance the state of machine learning-driven applications in the sciences,

ii) make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.

Particular areas of scientific application include (but are not limited to):

• Physics and space science

• Design and discovery of novel materials and molecules

• Materials characterisation techniques

• Simulation of materials, chemical processes and biological systems

• Atomistic and coarse-grained simulation

• Quantum computing

• Biology, medicine and biomedical imaging

• Geoscience (including natural disaster prediction) and climatology

• Particle Physics

• Simulation methods and high-performance computing

Conceptual or methodological advances in machine learning methods include those in (but are not limited to):

• Explainability, causality and robustness

• New (physics inspired) learning algorithms

• Neural network architectures

• Kernel methods

• Bayesian and other probabilistic methods

• Supervised, unsupervised and generative methods

• Novel computing architectures

• Codes and datasets

• Benchmark studies

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Machine Learning-science And Technology期刊信息

ISSN：2632-2153
出版语言：English
是否OA：开放
E-ISSN：2632-2153
出版地区：ENGLAND
是否预警：否
出版商：IOP PUBLISHING LTD
出版周期：Quarterly
创刊时间：2020
开源占比：0.9936
Gold OA文章占比：100.00%
OA被引用占比：
出版国人文章占比：0
出版撤稿占比：
研究类文章占比：97.82%

Machine Learning-science And Technology CiteScore评价数据（2026年6月最新版）

CiteScore

SJR

SNIP

CiteScore 指数

6.7

0.872

1.239

学科类别	分区	排名	百分位
大类：Computer Science 小类：Software	Q2	143 / 503	71%
大类：Computer Science 小类：Artificial Intelligence	Q2	166 / 570	70%
大类：Computer Science 小类：Human-Computer Interaction	Q2	73 / 196	63%

名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。

Machine Learning-science And Technology中科院评价数据

《新锐期刊分区表》（2026年3月发布）

Top期刊	综述期刊	大类学科		小类学科
否	否	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

Machine Learning-science And Technology JCR评价数据

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

Machine Learning-science And Technology历年数据统计

影响因子

中科院分区

Machine Learning-science And Technology中国学者发文选摘

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