APL Machine Learning

Aims & Scope

APL Machine Learning is a vibrant and timely research journal that focuses on two key communities: researchers who use machine learning (ML) and data-driven approaches for physical sciences and related disciplines, and researchers from these disciplines who work on novel concepts, including materials, devices, systems, and algorithms relevant for the development of better ML and AI technologies.

The journal considers research that substantially describes quantitative models and theories, especially if the research is validated with experimental results. Topics include, but are not limited to:

ML (machine learning) for AP (applied physics): Scientific ML, Interpretable ML for scientific discovery, ML-led physics-aware predictive models, and more. AP (applied physics) for ML (machine learning): Materials, devices and systems for ML/AI accelerators, Neuromorphic materials, devices and systems, and more.

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Directory of Open Access Journals (DOAJ)
• Google Scholar

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Chemical Engineering, Computer Science and Neuroscience, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Apc url
• Waiver url
• Homepage
• Aims scope

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Chemical Engineering, Computer Science and Neuroscience, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 2,750.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.