About 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

• Inclusive scope: the journal welcomes interdisciplinary studies and multidisciplinary collaborations across all areas of science.
• Open access: your paper will be published under a CC BY licence, enabling immediate and perpetual access, and permitting the widest possible dissemination and reuse of your research.
• High quality peer review: all articles will be rigorously peer reviewed by IOP Publishing’s global network of expert reviewers, supported by our top-level Editorial Board.
• Fast publication: we are committed to providing you with a fast, professional service to ensure rapid first decision, acceptance and publication. Once accepted, your article will be accessible to readers within 24 hours and will include a citable DOI.
• Pre-print friendly: you are encouraged to post on community pre-print platforms.
• Data and code: research published in Machine Learning: Science and Technology can include citable datasets and programmable code.
• Society owned: IOP Publishing is a leading society publisher of advanced physics research. Any profits generated by IOP Publishing are invested in the Institute of Physics, helping to support research, education and outreach around the world.
• Join our community: Machine Learning: Science and Technology is now on Twitter. Join our authors and editors to discuss the latest advances and applications of machine learning across the sciences @MLSTjournal.

Machine Learning: Science and Technology welcomes submissions of the following article types:

• Research papers: reports of original research work; normally not more than 8500 words.
• Letters: concise, high impact, original research articles that report a timely development in the field deserving of priority treatment and rapid publication. A letter should not normally be more than eight journal pages in length (5000 words). Authors submitting letters should provide justification as to why the work is urgent and requires rapid publication.
• Technical notes: short descriptors relating to datasets and code.
• Perspectives: commissioned commentaries from leading figures in the community that highlight the impact and wider implications of new research reported in Machine Learning: Science and Technology and elsewhere.
• Topical Reviews: written by leading researchers in their fields, these articles present the background to and overview of a particular field, and the current state of the art. Topical review articles are normally invited by the Editorial Board.
• Benchmark papers: seek to present examples of differing methods / models / codes /algorithms / software as applied to a consistent problem or dataset, report on the effectiveness of each example, and include comparative analysis of the outcomes. Benchmark papers may include original research and review material, as appropriate.
• Roadmaps: a collection of short 2-3 page perspectives on a topic. Each section has a different author(s), and the sections are combined together into one article for publication. Roadmaps capture the breadth of the hot topics in the field, and they are more forward looking than topical review articles. Each perspective should look at the history and status of the topic, current and future challenges, and advances in science and technology needed to address the challenges.

Code
We strongly support the principles of Open Source software. We encourage all authors of new code to comply with the Open Source Initiative. Where possible, code should be free to redistribute, and freely available as both source code and a compiled form. Authors should submit their code as supplementary material or provide links to an external repository.

We work with our authors to help make their work as easy to discover as possible. Machine Learning: Science and Technology is currently included in the following abstracting and discovery services:

• Scopus
• CNKI
• DOAJ
• Inspec
• J-Gate
• NASA Astrophysics Data System
• ProQuest Computing Database
• ProQuest Science Journals
• Scite
• Web of Science (Science Citation Index Expanded)