雜志介紹

Machine Learning-science And Technology雜志介紹

《Machine Learning-science And Technology》是一本以English為主的開放獲取國際優(yōu)秀期刊，中文名稱機器學(xué)習(xí)-科學(xué)與技術(shù)，本刊主要出版、報道物理與天體物理-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領(lǐng)域的研究動態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國際權(quán)威數(shù)據(jù)庫SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為6.3，最新CiteScore 指數(shù)為9.1。

本刊近期中國學(xué)者發(fā)表的論文主要有：

• Machine learning phases in swarming systems

  Author: Xue, Tingting; Li, Xu; Chen, Xiaosong; Chen, Li; Han, Zhangang

• Deep learning representations for quantum many-body systems on heterogeneous hardware

  Author: Liang, Xiao; Li, Mingfan; Xiao, Qian; Chen, Junshi; Yang, Chao; An, Hong; He, Lixin

英文介紹

Machine Learning-science And Technology雜志英文介紹

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