Machine Learning: Science and Technology
IOP Publishing
About the journal
Machine Learning: Science and Technology is an open-access journal published by IOP Publishing in United Kingdom, publishing in english. It is listed in DOAJ and PUBMED according to the sources read. Declared publication charge: 2,500 GBP. Declared review time at DOAJ: 20 weeks. Facts are reproduced as recorded at their sources with their capture dates; PublishLens indicators are available after sign-in.
Basic facts
- Publisher
- IOP Publishing
- ISSN
- 2632-2153
- Country
- United Kingdom
- Languages
- english
- Access
- Open access
- Declared publication charge
- 2,500 GBP
- Articles in DOAJ
- 1109
- Declared review time
- 20 weeks
- Declared peer-review model
- double_blind
- Official website
- https://iopscience.iop.org/journal/2632-2153
Index listings
- DOAJ · 2020-04-14
- PUBMED · 2022-12-01
- Indexing
- DOAJ · PUBMED
- APC
- 2,500 GBP
- Declared review time (DOAJ)
- 20 weeks — journal-declared only (not measured)
- Activity (OpenAlex)
- Active — latest works 2026
- PublishLens list status
- Shown after sign-in
- Counts for promotion at your university?
- Choose your university and rank below ↓
Methodology signals (dated facts from their sources)
Details of the evaluation under the published methodology are shown to signed-in users (free).
Create a free account to view the evaluationJournal profile
Descriptive metadata from public sources (DOAJ, OpenAlex, PubMed) as of the capture date shown — contains no assessment or judgment.
Articles in DOAJ
1109
Most recent article
last month
Average review time (journal-declared via DOAJ)
20weeks
Works in OpenAlex
1388
Last recorded publication year
2026
h-index
49
i10-index
368
From OpenAlex open data (CC0).
Editorial & policy13
- Last DOAJ review date
- 2026-07-24
- Added to DOAJ
- 2020-04-14
- Declared plagiarism screening
- Declared
- Other fees beyond APC
- No
- Declared digital preservation
- CLOCKSSPortico
- Observed digital preservation (Keepers registry)— snapshot 2026-10-05
- CLOCKSSPortico
- Editorial board
- Official page ↗
- Norwegian register (HK-dir)— 2026
- Level 1 — approved scholarly channel
- Open references deposited at Crossref
- 98%
- ORCID iDs in current deposits
- 99%
- Enrolled in Crossref Similarity Check
- ✓
- Deposits licence metadata at Crossref
- ✓
- APC waiver policy
- View policy
- Peer-review policy
- View policy
Identity & continuity2
- Source type
- journal
- NLM unique ID (PubMed)
- 101774789
Classification4
Subjects (DOAJ)
LCC codes
Subject areas
- Machine Learning in Materials Science
- Machine Learning in Materials Science
- Model Reduction and Neural Networks
- Model Reduction and Neural Networks
- Quantum Computing Algorithms and Architecture
- Quantum Computing Algorithms and Architecture
- Quantum many-body systems
- Computational Drug Discovery Methods
Show all subjects (50)
- Neural Networks and Applications
- Neural Networks and Applications
- Quantum many-body systems
- Computational Drug Discovery Methods
- Gaussian Processes and Bayesian Inference
- Gaussian Processes and Bayesian Inference
- Particle physics theoretical and experimental studies
- Particle physics theoretical and experimental studies
- Computational Physics and Python Applications
- Computational Physics and Python Applications
- Neural Networks and Reservoir Computing
- Neural Networks and Reservoir Computing
- Protein Structure and Dynamics
- Quantum Information and Cryptography
- Protein Structure and Dynamics
- Quantum Information and Cryptography
- Particle Detector Development and Performance
- Particle Detector Development and Performance
- Advanced Electron Microscopy Techniques and Applications
- Generative Adversarial Networks and Image Synthesis
- Generative Adversarial Networks and Image Synthesis
- Advanced Electron Microscopy Techniques and Applications
- Anomaly Detection Techniques and Applications
- Anomaly Detection Techniques and Applications
- Advanced Neural Network Applications
- Domain Adaptation and Few-Shot Learning
- Radiation Detection and Scintillator Technologies
- Radiation Detection and Scintillator Technologies
- Domain Adaptation and Few-Shot Learning
- X-ray Diffraction in Crystallography
- Electron and X-Ray Spectroscopy Techniques
- X-ray Diffraction in Crystallography
- Advanced Neural Network Applications
- Electron and X-Ray Spectroscopy Techniques
- Machine Learning and Data Classification
- Pulsars and Gravitational Waves Research
- Machine Learning and Data Classification
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
- Adversarial Robustness in Machine Learning
- Pulsars and Gravitational Waves Research
- Advanced Graph Neural Networks
Keywords (DOAJ)
- machine learning
- artificial intelligence
- computational physics
- reinforcement learning
- neural networks
- applied algorithms
Community confirmations — what users saw in MJL or Scopus Sources; not platform verification and never part of any score.
The journal's record in other sources:Web of Science MJLDOAJ