Journal profile · Computer Science-Artificial Intelligence
Machine Learning-Science and Technology
ISSN 2632-2153 · IOP PUBLISHING LTD · ENGLAND · Quarterly · Founded 2020
Machine Learning-Science and Technology: CAS Zone 2 · Physics & Astronomy (2025 upgraded edition); impact factor 4.13; fully open access; APC about USD 3,125; median 131 days to acceptance (96 articles, last 24 months); 316 articles in 2025; indexed in SCIE and Scopus.
Scope
Aims, scope and focus
Machine Learning-Science and Technology is published by IOP PUBLISHING LTD and is in Zone 2 of Physics & Astronomy in the CAS Journal Ranking (2025 upgraded edition). By OpenAlex counts of its recent articles, its largest topics are Machine Learning in Materials Science, Model Reduction and Neural Networks and Quantum Computing Algorithms and Architecture, together about 36.2% of its articles. Its main subfields are Artificial Intelligence. (generated from OpenAlex topic counts and CAS ranking data)
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
From the journal's Aims & Scope
Research area (LetPub): Computer Science-Artificial Intelligence
Editor-in-chief Michael E. FlattéPublisher website
Editorial board by country: 2 in mainland China (about 6%); most named United Kingdom 9, United States 7, Germany 4, Canada 3, Mainland China 2 (36 members with a country) Editorial board ↗Publisher website ·
Main research topics OpenAlex counts of recent articles; shares are approximate
- Machine Learning in Materials ScienceMaterials Science21.4%
- Model Reduction and Neural NetworksPhysics and Astronomy8%
- Quantum Computing Algorithms and ArchitectureComputer Science6.8%
- Quantum many-body systemsPhysics and Astronomy6%
- Computational Drug Discovery MethodsComputer Science5.7%
- Neural Networks and ApplicationsComputer Science5.6%
- Gaussian Processes and Bayesian InferenceComputer Science4.8%
- Particle physics theoretical and experimental studiesPhysics and Astronomy4.3%
Rankings & indexing
CAS ranking and database indexing
CAS Journal Ranking
CAS Journal Ranking, upgraded edition (March 2025)Physics & Astronomy Zone 2
| Level | Category | Zone |
|---|---|---|
| Major | Physics & Astronomy | Zone 2 |
| Minor | Multidisciplinary Sciences | Zone 2 |
| Minor | Computer Science, Artificial Intelligence | Zone 3 |
| Minor | Computer Science, Interdisciplinary Applications | Zone 3 |
Top journal: noReview journal: no
CAS Xinrui Journal Ranking (March 2026)Physics & Astronomy Zone 2
| Level | Category | Zone |
|---|---|---|
| Major | Physics & Astronomy | Zone 2 |
| Minor | Computer Science, Artificial Intelligence | Zone 3 |
| Minor | Computer Science, Interdisciplinary Applications | Zone 3 |
Top journal: noReview journal: not marked
CAS Journal Ranking, previous upgraded edition (December 2023)Physics & Astronomy Zone 2
| Level | Category | Zone |
|---|---|---|
| Major | Physics & Astronomy | Zone 2 |
| Minor | Computer Science, Artificial Intelligence | Zone 2 |
| Minor | Multidisciplinary Sciences | Zone 2 |
| Minor | Computer Science, Interdisciplinary Applications | Zone 3 |
Top journal: noReview journal: no
Database indexing
SCIEScopusDOAJLetPub
International journal early-warning lists: not listed in any editionLetPub
Indexing the journal's website claims
Web of ScienceINSPECPublisher website
Other: ProQuest, CNKI, J-Gate, Astrophysics Data System
As the journal's website lists them, not checked against each database; for Web of Science editions the records above apply.
Editions, and major and minor categories, are shown separately and never merged into one zone; check which edition your institution uses before you submit or report.
Metrics
Impact metrics and publication volume
Impact
- Impact factor
- 4.13LetPub ·
- CiteScore 2026-06
- 6.7LetPub ·
- SJR 2026-06
- 0.87LetPub ·
- SNIP 2026-06
- 1.24LetPub ·
- h-index
- 55OpenAlex ·
- i10-index
- 442OpenAlex ·
- 2-year mean citedness
- 3.84OpenAlex ·
- Self-citation rate
- 7.1%LetPub ·
- Share of gold OA articles
- 100%LetPub ·
Volume
- Articles per year
- 321LetPub ·
Articles by year OpenAlex count · 1,495 in total · the current year is incomplete
| Year | Articles | Open access | Citations |
|---|---|---|---|
| 2026 | 291 | 291 | 123 |
| 2025 | 316 | 316 | 856 |
| 2024 | 344 | 344 | 2,676 |
| 2023 | 200 | 200 | 2,552 |
| 2022 | 96 | 96 | 2,038 |
| 2021 | 112 | 112 | 3,982 |
Sources count differently and were observed at different times, so each source's own value is kept; "publisher" marks figures from the journal's website.
Review & acceptance
Review time and acceptance
Review time from article dates manuscript dates registered with Crossref · to
131 daysMedian, submission to acceptance · P25–P75 91 days – 172 days
- Acceptance to online
- median 15 days (96 articles)
- Submission to online
- median 147 days
- Sample
- 96 articles with submission and acceptance dates (705 in the window); the latest was accepted on
Only articles that were accepted and published are counted, not the time spent on rejected manuscripts, so this usually runs longer than a publisher's "time to first decision".
Other sources
- Average review time (community reports)
- about 5 weeksLetPub ·
- Submission to publication (journal-reported)
- about 20 weeksDOAJ ·
- Peer review
- Double anonymous peer review, Single anonymous peer reviewDOAJ
Community reports come from authors' own submissions (confidence 0.5) and are not a promise from the journal; for publisher figures, the journal's own definitions apply.
Fees & open access
APC and open-access policy
Fully open accessDOAJ
Every article is open access; authors usually pay an article processing charge (APC).
| Source | APC | Fee waiver | License | Observed |
|---|---|---|---|---|
| DOAJ | GBP 2,500 / EUR 3,000 / USD 3,125 | Yes | CC BY | |
| OpenAlex | USD 3,000 / EUR 2,595 / GBP 2,185 | — | — | |
| LetPub | USD 2,610 / EUR 2,255 / GBP 1,900 | Yes | — | |
| Publisher website | GBP 2,500 / EUR 3,000 / USD 3,125 | — | — |
The journal's website is authoritative for fees; sources were observed at different times and each keeps its own value, without averaging.
Submission
Article types and submission policies (journal website)
Policy excerpts from the journal's websiteLicense & copyright, Open access, Peer review
- License & copyright Copyright and permissions
View information and resources to help you with our copyright and permissions policies.
Source ↗Publisher website ·- Open access Open access and transformative agreements
Find out about open access publishing options, policies, prices and transformative agreements at IOP Publishing.
Source ↗Publisher website ·- Peer review Peer review
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 …
Source ↗Publisher website ·
Excerpts are quoted from the journal's website; "model summary" lines are written by Workers AI and checked against the text. The journal's website is authoritative.
Profile
Publication details and official links
- Publisher
- IOP PUBLISHING LTD
- Country or region
- ENGLAND
- Language
- English
- Frequency
- Quarterly
- Founded
- 2020
- ISSN
- 2632-2153
- Official links
- Journal homepage ↗LetPub
- Submission system ↗LetPub
- Author guidelines ↗LetPub
- Aims & Scope ↗DOAJ
- Special issues ↗Publisher website
- APC information ↗DOAJ
- Fee waivers ↗DOAJ
- Open access statement ↗DOAJ
- License & copyright ↗DOAJ
- Peer review process ↗DOAJ
- Editorial board ↗DOAJ
- Plagiarism policy ↗DOAJ
FAQ
Frequently asked questions about Machine Learning-Science and Technology
What CAS zone is Machine Learning-Science and Technology in?
In the 2025 upgraded edition of the CAS Journal Ranking, Machine Learning-Science and Technology is in Zone 2 of Physics & Astronomy, and in Zone 2 of the minor category Multidisciplinary Sciences / Computer Science, Artificial Intelligence / Computer Science, Interdisciplinary Applications. In the 2026 Xinrui edition it is in Zone 2 of Physics & Astronomy. Zones from different editions, and from major and minor categories, are not interchangeable.
What is the impact factor of Machine Learning-Science and Technology?
The latest impact factor listed by LetPub is 4.13; CiteScore 6.7, h-index 55.
How long does peer review take at Machine Learning-Science and Technology?
Based on the dates recorded on 96 articles published between October 2024 and October 2026 (the last 24 months), the median time from submission to acceptance is 131 days (interquartile range 91–172 days); authors report an average review time of about 5 weeks. Article statistics cover accepted papers only, publisher figures follow the journal's own definitions, and community reports come from authors' own submissions; none is a promise from the journal.
Where are the editorial board members of Machine Learning-Science and Technology based?
Of the 36 board members whose country the journal's board page names, 2 (about 6%) are in mainland China; the countries named most often are United Kingdom (9), United States (7) and Germany (4). Counted from the institutions listed on the board page; for reference only.
Which databases index Machine Learning-Science and Technology?
According to LetPub and DOAJ records, Machine Learning-Science and Technology is indexed in SCIE, Scopus and DOAJ. The journal's website also lists Web of Science and INSPEC; that list has not been checked against each database.
Is Machine Learning-Science and Technology open access, and what is the APC?
Machine Learning-Science and Technology is a fully open-access journal. APC about USD 3,125.
Sources and updates
The facts on this page come from LetPub, OpenAlex, DOAJ, Publisher website; every figure is tagged with its source and the date it was observed, and conflicting values are kept per source. LetPub source page: view ↗ (archived 2026-08-26). Last updated: .