About the journal
Machine Learning: Science and Technology is a multidisciplinary journal that bridges the application of machine learning across a broad range of subject disciplines (extending to physics, materials science, chemistry, biology, biomedicine, earth science and space science). A particular focus is on new conceptual advances in machine learning methods motivated by the physical sciences. Particular areas of interest include (but are not limited to):
- Atomistic and coarse-grained simulation
- Design and discovery of novel materials and molecules
- Quantum machine learning
- Simulation of molecules, chemical processes and biological systems
- Applications of machine learning in physics and space science
- Applications of machine learning in biomedicine and imaging
- Applications of machine learning in geoscience (including natural disaster prediction) and climatology
- Neuro-inspired computing (including neuromorphic computing)
- New conceptual advances in machine learning methodology motivated by physical insights
- Applied algorithms and high-performance computing
Why publish in MLST?
- Scope: Representative of all strands and sub-disciplines within machine learning and the physical sciences, with a focus on emerging areas of high interest within the community.
- High standards: Selective editorial policy to ensure publication of only the highest quality research in terms of significance, originality and scientific rigour.
- Open access: Articles are published under a CC-BY license to maximize accessibility, engagement and collaboration and to support open science. The article publishing charge is waived through July 2020, meaning publication in the journal is free for all authors.
- 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.
- Transfer opportunities: As well as accepting direct submissions, the journal also offers you a quick and easy solution to transfer your manuscript from another IOP Publishing journal if it does not fit that journal’s scope or significance criteria. Articles are transferred along with peer review reports to save time and avoid duplication of work for referees.
- Peer review: Submissions are peer-reviewed by a dedicated, experienced team at IOP Publishing, supported by an international Editorial Board comprising experts from across the full breadth of complex systems and networks.
- 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.