Machine Learning-science And TechnologySCIE

英文簡介Machine Learning-science And Technology期刊介紹

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

期刊簡介Machine Learning-science And Technology期刊介紹

《Machine Learning-science And Technology》自2020出版以來，是一本物理與天體物理優秀雜志。致力于發表原創科學研究結果，并為物理與天體物理各個領域的原創研究提供一個展示平臺，以促進物理與天體物理領域的的進步。該刊鼓勵先進的、清晰的闡述，從廣泛的視角提供當前感興趣的研究主題的新見解，或審查多年來某個重要領域的所有重要發展。該期刊特色在于及時報道物理與天體物理領域的最新進展和新發現新突破等。該刊近一年未被列入預警期刊名單，目前已被權威數據庫SCIE收錄，得到了廣泛的認可。

該期刊投稿重要關注點：

• 預計審稿時間：約Submission to first decision before peer review: 3 days; Submission to first decision after peer review: 49 days; 13 Weeks
• 國際TOP期刊
• 物理與天體物理
• COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
• SCIE
• 中科院2區
• 非預警