Machine Learning series

The home for machine learning across the sciences

IOP Publishing’s Machine Learning series™ is the world’s first open access journal series dedicated to the application and development of machine learning (ML) and artificial intelligence (AI) for the sciences. The series offers an evolving network of open access journals, and builds on the success of Machine Learning: Science and Technology with the same high publishing standards and innovations.

Visibility – maximise the reach of your research by publishing in one of our fully open access journals.

Flexible and innovative article formats – In addition to research articles and reviews you can publish dataset articles, benchmarks and challenges. Recognising the diverse publishing needs of researchers working at the interface of Machine Learning and AI across the sciences.
Personal touch – our publishing experts are always on hand to provide guidance and advice throughout the whole publication process.
Leadership in peer review – our Machine Learning journal series benefits from exceptionally high standards and innovations in peer review. IOP Publishing is recognised by the publishing industry for the introduction of double-anonymous and transparent peer review across all our peer reviewed journals.
Purpose-led publishing – Science is our only shareholder which means that we always put science and research integrity before profit. 100% of our revenue is invested back into science.

Discover the Machine Learning series

Machine Learning: Science and Technology

Editor-in-Chief
Kyle Cranmer
University of Wisconsin-Madison, USA

Submit to Machine Learning: Science and Technology

Launched in 2019, 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.

9.1

CiteScore

6.3

Impact factor

Machine Learning: Engineering

Editor-in-Chief
Jay Lee
University of Maryland, USA

Submit to Machine Learning: Engineering

Machine Learning: Engineering is dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of engineering. This multidisciplinary open access journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to all areas of engineering.

Machine Learning: Earth

Editor-in-Chief
Pierre Gentine
Columbia University, USA

Submit to Machine Learning: Earth

Machine Learning: Earth is dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of earth, environmental and climate sciences including efforts to ensure a sustainable future. This multidisciplinary open access journal publishes research reporting data-driven approaches that advance our knowledge of the Earth system, and of the interactions between biosphere, hydrosphere, cryosphere, atmosphere and geosphere.

Machine Learning: Health

Editor-in-Chief
Jimeng Sun
University of Illinois, USA

Submit to Machine Learning: Health

Machine Learning: Health is dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across healthcare and the medical, biological, clinical, and health sciences. This multidisciplinary open access journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to medicine and health sciences.

A home for your research

We are committed to finding a home for every publishable paper across the Machine Learning series. As part of the submission process your research will be matched to the right journal with tailored transfer options across the series. Simply submit your research to your journal of choice and our team will endeavour to find the most suitable home for your paper in our Machine Learning titles.

Our publishing experts are always on hand to provide advice and guidance on where best to publish, contact us today to find out more about our tailored submission process.