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Machine Learning: Science and Technology - Institute of Physics Publishing | 2024 Impact factor:4.6 | Citescore: 7.7 | Q1

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Machine Learning: Science and Technology - Institute of Physics Publishing | 2024 Impact factor:4.6 | Citescore: 7.7 | Q1

  • About this Journal:
  • Machine Learning: Science and Technology (MLST) is a peer-reviewed journal published by IOP Publishing, focusing on high-quality research at the intersection of machine learning theory, methodology, and real-world scientific and technological applications.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
  • The journal highlights rigorous studies that advance the understanding, design, and implementation of machine learning systems, with an emphasis on scientific reproducibility, transparency, and technical depth.
  • MLST publishes research articles, reviews, perspectives, and technical notes spanning innovations in ML algorithms, engineering methods, and domain-specific applications.
  • It serves as a platform connecting researchers in AI, data science, engineering, computational science, physics, healthcare, and other application-driven fields.

  • Editor-in-Chief:  Kyle Cranmer

  • Scope of the Journal:
  • Foundational research on machine learning algorithms, models, and optimization methods.
  • Machine learning applications in scientific discovery, engineering systems, materials science, physics, chemistry, biology, and healthcare.
  • Deep learning architectures, interpretability, model evaluation, and benchmarking.
  • Data-centric ML approaches including data quality, labeling, preprocessing, and dataset design.
  • ML for simulation, prediction, control, and automation in complex scientific and industrial environments.
  • Human–AI interaction, reliability, robustness, uncertainty quantification, and trustworthy ML.
  • Integration of ML with high-performance computing, sensor technologies, and experimental workflows.
  • Hybrid physics-informed ML, scientific ML frameworks, and computational modeling enhancements.
  • Latest Research Topics for PhD in Computer Science
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  • Latest Research Topics for PhD in Machine Learning
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  • Latest Research Topics for PhD in Artificial Intelligence
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  • Print ISSN:  

    Electronic ISSN:  26322153

  • Abstracting and Indexing:  Scopus, SCIE

  • Imapct Factor 2024:  4.6

  • Subject Area and Category:  Artificial Intelligence, Human-Computer Interaction, Software

  • Publication Frequency:  

  • H Index:  41

  • Best Quartile:

    Q1:  Artificial Intelligence

    Q2:  

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    Q4:  

  • Cite Score:  7.7

  • SNIP:  1.392

  • Journal Rank(SJR):  1.119