Machine Learning-science And Technology

Journal Title：Machine Learning-science And Technology

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

《机器学习：科学与技术》是一本多学科的开放获取期刊，它将机器学习在各个科学领域的应用与受物理洞察推动的机器学习方法和理论的进步联系起来。具体而言，文章必须属于以下类别之一：

i) 推动机器学习驱动的科学应用发展，

或

ii) 在机器学习方面取得概念、方法或理论进步，应用于科学问题、从科学问题中得到启发或受其激励。

科学应用的特定领域包括（但不限于）：

• 物理学和空间科学

• 新型材料和分子的设计和发现

• 材料表征技术

• 材料、化学过程和生物系统的模拟

• 原子和粗粒度模拟

• 量子计算

• 生物学、医学和生物医学成像

• 地球科学（包括自然灾害预测）和气候学

• 粒子物理学

• 模拟方法和高性能计算

机器学习方法中的概念或方法论进步包括（但不限于）：

• 可解释性、因果关系和稳健性

• 新的（受物理启发的）学习算法

• 神经网络架构

• 核方法

• 贝叶斯和其他概率方法

• 监督、无监督和生成方法

• 新型计算架构

• 代码和数据集

• 基准研究

Machine Learning-science And Technology创刊于2020年，由IOP PUBLISHING LTD出版商出版，收稿方向涵盖Multiple全领域，此刊是该细分领域中属于非常不错的SCI期刊，在行业细分领域中学术影响力较大，专业度认可很高，所以对原创文章要求创新性较高，如果您的文章质量很高，可以尝试。平均审稿速度约Submission to first decision before peer review: 3 days; Submission to first decision after peer review: 49 days; 13 Weeks ，影响因子指数6.3，该期刊近期没有被列入国际期刊预警名单，广大学者值得一试。