Academic Journal
Q2Machine Learning: Science and Technology
About Machine Learning: Science and Technology
Machine Learning: Science and Technology is a scholarly journal published by Institute of Physics Publishing. SCImago 2025 lists it in Q2, with an SJR of 0.872 and H-index of 48.
Coverage: 2020-2026. Research categories: Artificial Intelligence (Q2); Human-Computer Interaction (Q2); Software (Q2).
Open-access policies and author information
Reported in the official DOAJ public CSV snapshot (2026-09-01), downloaded 2026-10-03. Record updated 2026-07-24. This snapshot does not establish today’s listing status or fee quotation.
Publisher policy links recorded by DOAJ
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Source: DOAJ journal record. Journal metadata is distributed by DOAJ under CC0. Confirm current fees, tax, eligibility and waiver terms with the publisher.
Source-backed journal facts
Topics in published research
Machine Learning in Materials Science; Model Reduction and Neural Networks; Quantum Computing Algorithms and Architecture; Quantum many-body systems; Computational Drug Discovery Methods; Neural Networks and Applications.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
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2026-09-24 · DOI: 10.1088/2632-2153/aeac45QuantumCanvas: a multimodal benchmark for learning two-body quantum interactions
Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban et al.
2026-10-01 · DOI: 10.1088/2632-2153/aea5d6Reviews
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October 4, 2026 at 9:02 pm
October 2, 2026