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Journal of Physics: Materials

eISSN: 2515-7639
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Publisher: IOP Publishing
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Engineering(all)Building and ConstructionMaterials Science(all)Condensed Matter PhysicsAtomic and Molecular Physics, and Optics

JPhys Materials™ is an innovative new open access journal for high quality research in materials science, focusing in particular on interdisciplinary and multidisciplinary approaches. It builds on the strength and prestige of the Journal of Physics series, which celebrated 50 years of publishing in 2017. The journal will showcase the most significant and exciting developments in materials science research and apply open science principles to encourage maximum collaboration, reproducibility and dissemination of research. It is firmly focused on a community-oriented approach to communicating science and is not driven by funders, institutions or for-profit corporations.

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Hybrid Journal

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Transformative Agreement

Select the open access publishing option with a https://creativecommons.org/licenses/by/2.0/ license to ensure compliance.

Transformative Journal

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Full Open Access

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Self-Archiving

Following acceptance, please deposit your author accepted manuscript in a repository without embargo.

Self-Archiving using Rights Retention

Your funder’s grant conditions set out how you can retain sufficient rights to self-archive the Author Accepted Manuscript in any OA repository. Publishing fees do not apply with this route.

Unfortunately, this journal has no Plan-S compliant options - Provided by Journal Checker Tool

List Price

EUR 2,255.00
GBP 1,900.00
USD 2,615.00
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Machine Learning: Engineering

eISSN: 3049-4761
Publisher: IOP Publishing
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HistoryChemical Engineering(all)BioengineeringComputer Science(all)Artificial IntelligenceEngineering(all)Aerospace EngineeringAutomotive EngineeringBiomedical EngineeringCivil and Structural EngineeringControl and Systems EngineeringElectrical and Electronic EngineeringIndustrial and Manufacturing EngineeringMechanical EngineeringMechanics of MaterialsOcean EngineeringBuilding and Construction

Machine Learning: Engineering is a multidisciplinary open access journal dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of engineering. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to engineering.

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Compliant Options - Provided by Journal Checker Tool

Hybrid Journal

Go ahead and submit. Remember to select a CC BY licence to ensure compliance.

Transformative Agreement

Select the open access publishing option with a https://creativecommons.org/licenses/by/2.0/ license to ensure compliance.

Transformative Journal

Select the open access publishing option with a CC BY license to ensure compliance.

Full Open Access

Go ahead and submit. Remember to select a CC BY license to ensure compliance.

Self-Archiving

Following acceptance, please deposit your author accepted manuscript in a repository without embargo.

Self-Archiving using Rights Retention

Your funder’s grant conditions set out how you can retain sufficient rights to self-archive the Author Accepted Manuscript in any OA repository. Publishing fees do not apply with this route.

Unfortunately, this journal has no Plan-S compliant options - Provided by Journal Checker Tool

As part of our commitment to open science, IOP Publishing is covering the article publication charge (APC) for all articles published in the journal.
As part of our commitment to open science, IOP Publishing is covering the article publication charge (APC) for all articles published in the journal.
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