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,
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
Why should you publish in Machine Learning: Science and Technology?
- 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.
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.
The following summary describes the peer review process for Machine Learning: Science and Technology, using the ANSI/NISO Standard Terminology for Peer Review:
- Identity transparency: single-anonymous, double-anonymous (author choice)
- Reviewer interacts with: Editor
- Review information published: review reports (author and reviewer opt in), author/editor communication, reviewer identities (reviewer opt in)
Our Publishing Support website provides more information on our reviewing process as well as checklists in both English and Chinese language to help authors prepare their manuscripts for submission.
If an article is not accepted for publication, we may offer the author the opportunity to transfer their submission to other suitable journals we publish.
Inclusivity and diversity
IOP Publishing recognises that there are inequalities within the scientific publishing and research ecosystems. We are committed to a progressive approach to inclusivity and diversity, and are working hard to eliminate discrimination to foster an equitable and welcoming publishing environment for all.
IOP Publishing follows Guidelines on Inclusive Language and Images in Scholarly Communication to ensure that journal articles use bias-free and culturally sensitive communication. We ask authors to please follow these guidelines in their manuscript submissions.
More information about our work on inclusivity is available on our Open Physics hub.
Please note that this policy requires authors to include a data availability statement in their article.
For any questions about the policy please contact the journal.
Many research funders now require authors to make all data related to their research available in an online repository. Please refer to the policy for further information about research data, data repositories and data citation.
For more information on IOP Publishing’s open access policies please see our Open access page.
|Article publication charge*||£1750||€2075||$2405|
|Reduced article publication charge* for Group B countries**||£500||€575||$675|
|Reduced article publication charge for Group A countries**||£0||€0||$0|
*excluding VAT where applicable
**eligibility criteria can be found here
APCs only apply to articles accepted for publication; there are no submission charges.
There are no other charges for publishing in Machine Learning: Science and Technology.
Transformative AgreementsMachine Learning: Science and Technology is included in our transformative agreements which allow authors from some institutions to publish open access without paying an APC.
If you are covered by an agreement, use our author guide to help you submit your paper.
Countries where we have transformative agreements include:
Austria, Canada, Croatia, Finland, Hungary, Ireland, Israel, Germany, Poland, The Netherlands, United Kingdom, Saudi Arabia, Slovenia, Sweden and Switzerland.
Paying for open access
Various discounts, waivers and funding arrangements are available to support our authors. Visit our Paying for open access page for further details.
Abstracting and indexing services
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:
- NASA Astrophysics Data System
- ProQuest Computing Database
- ProQuest Science Journals
- Web of Science (Science Citation Index Expanded)