AIP Publishing LLC
AIP Publishing LLC
Your Window to Possible
  • Scitation
  • AIP
  • AIP China
  • University Science Books
  • Resources
    • Researchers
    • Librarians
    • Publishing Partners
    • Commercial Partners
  • Publications

    Find the Right Journal

    Explore the AIP Publishing collection by title, topic, impact, citations, and more.
    Browse Journals

    Latest Content

    Read about the newest discoveries and developments in the physical sciences.
    See What's New

    Publications

    • Journals
    • Books
    • Physics Today
    • Conference Proceedings
    • Databases
    • Scilight
    • Find the Right Journal
    • Latest Content
  • About
    • About Us
    • News and Announcements
    • Diversity, Equity, and Inclusion
    • Careers
    • Events
    • Leadership
    • Contact
  • Scitation
  • AIP
  • AIP China
  • University Science Books
  • Journals
  • Upcoming Special Topics
  • The Journal of Chemical Physics
  • Software for Atomistic Machine Learning

Software for Atomistic Machine Learning

Submission Deadline: December 16, 2022Contribute to this Special Topic

The application of machine-learning techniques to atomistic modeling of physics, chemistry and materials science is blooming, and machine learning is becoming an integral part of the toolbox of molecular simulations. As the conceptual framework underlying these techniques become mainstream, the software infrastructure used to apply machine learning to atomistic problems is also evolving, from experimental code to carefully designed, user-friendly, feature-rich and efficient libraries that implement state-of-the-art methods. This special issue welcomes contributions that present a snapshot of this implementation effort, discussing creative solutions of outstanding problems, demonstrating the efficiency and scaling of algorithms, and providing examples of applications to difficult modeling problems.

Software presented in this special issue will need to be “easily available” to academics. There are two aspects of availability that determine how easy it is. The first one is cost: alongside free (as in beer) software, academic versions of software costing a few hundred dollars we also classify as easily available. The second aspect is transparency. The process for obtaining the software needs to be public and not discriminate unduly: the code must be obtainable by all those who are willing to accept simple and conventional licensing terms, without any undue burden of collaboration or constraints on the intended use of the software.


Guest Editors

Gábor Csányi, University of Cambridge

Matthias Rupp, University of Konstanz

Emine Kucukbenli, Boston University and Harvard University

JCP Editors

Michele Ceriotti, EPFL, Institute of Materials

David Manolopoulos, University of Oxford

Angelos Michaelides, University of Cambridge

David Sherrill, Georgia Institute of Technology


More information:

Please note that papers will be published as normal when they are ready in a regular issue of the journal and will populate on a virtual collection page within a few days of publication. Inclusion in the collection will not cause delay in publication.


How to submit:

  • Please submit through the online submission system.
  • Under manuscript type → select Article or Communication, as appropriate.
  • Under manuscript information → Title/Abstract → select “Invited Submission: No”.
  • Under manuscript information → Manuscript classification → select Special Topic: “Software for Atomistic Machine Learning”
Submission Deadline: December 16, 2022Contribute to this Special Topic
  • Featured
  • Coming Soon: APL Energy
  • Coming Soon: APL Machine Learning
  • COVID-19
  • Upcoming Special Topics
  • Submit Your Article
  • Visit AIP Author Services
  • Sign Up for News

Expand the impact of your findings.

Researcher Resources

Manage your foundational research collections.

Librarian Resources

Resources

  • Researchers
  • Librarians
  • Publishing Partners
  • Commercial Partners

About

  • About Us
  • Careers
  • Events
  • Leadership

Support

  • Contact / Customer Service
  • Terms Of Use
  • Privacy Policy

Newsletter

Sign up to receive the latest news and information from AIP Publishing.
Sign Up Now
  • © 2022 AIP Publishing LLC
  • Site created by Windmill Strategy