Machine Learning-Science and Technology

期刊名

Machine Learning-Science and Technology

MACH LEARN-SCI TECHN

ISSN / eISSN

2632-2153 / 2632-2153

目标和范围

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,

or

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

研究方向

计算机：人工智能

综合性期刊

计算机：跨学科应用

CiteScore

9.10 查看趋势图

CiteScore 学科排名

类别	分区	排名
Computer Science - Software	Q1	#70/407
Computer Science - Human-Computer Interaction	Q1	#26/145
Computer Science - Artificial Intelligence	Q1	#73/350

Web of Science 核心收藏夹

Science Citation Index Expanded (SCIE)	Social Sciences Citation Index (SSCI)
Indexed	-

类别 (Journal Citation Reports 2024)	分区
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	Q1
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	Q1
MULTIDISCIPLINARY SCIENCES	Q1

出版国家或地区

ENGLAND

出版商

IOP PUBLISHING LTD

出版周期

Quarterly

出版年份

2020

年文章数

194

Open Access

YES

通讯方式

IOP PUBLISHING LTD, TEMPLE CIRCUS, TEMPLE WAY, BRISTOL, ENGLAND, BS1 6BE