IOP Publishing launches new journal series

IOP Publishing (IOPP) has announced it is launching a series of open access journals dedicated to the application and development of machine learning (ML) and artificial intelligence (AI) for the sciences. The new multidisciplinary Machine Learning series will collectively cover applications of ML and AI across the physical sciences, engineering, biomedicine and health, and environmental and earth science.

Building on the successful launch of Machine Learning: Science and Technology in 2019, IOPP says its Machine Learning series will expand to include three new journals: Machine Learning: Health, Machine Learning: Earth, and Machine Learning: Engineering. The new journals will open for submissions later this year. In addition to research articles and reviews the series will also publish dataset, benchmark and challenge articles to meet the diverse needs of research communities working at the interface of ML, AI and the sciences.

“It’s clear that ML and AI have the potential to be transformational in accelerating the advance of new scientific knowledge and discovery,” says Dr. Tim Smith, head of portfolio development at IOPP. “Through our new Machine Learning series, we’re committed to creating a world-leading publishing home that represents the many areas of science where ML is already playing a critical role. We’re excited to introduce even more article formats supporting our open science goals as a publisher and ensuring the reproducibility, integrity and trust of peer-reviewed research.”

Authors publishing in IOPP’s three new machine learning journals will benefit from free open access publishing throughout 2025, with all article publication charges covered by IOPP, added the publisher.

As part of the Purpose-Led Publishing coalition, IOPP says researchers publishing with them can be assured that their work will advance knowledge and contributes to the physical sciences community. Profits generated by IOPP are reinvested into the Institute of Physics, supporting efforts to make science accessible for all.

For more information about the Machine Learning series, click here.