Collection 

Machine Learning Interatomic Potentials in Computational Materials

Submission status
Closed
Submission deadline

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection is to showcase cutting-edge developments in MLIP architectures, data generation techniques, and innovative sampling methods that push the boundaries of accuracy, efficiency, and applicability in atomic-scale simulations.

This Collection welcomes the following topics, including but not limited to:

  1. Architectural Advances
    1. New model architectures that enhance the accuracy, interpretability, or transferability of interatomic potentials.
    2. Novel algorithms for reducing model complexity while maintaining predictive power.
  2. Data Generation and Processing
    1. High-quality data generation strategies, including ab initio data curation and active learning techniques that minimize the need for computationally expensive data.
    2. Approaches for ensuring data diversity and coverage to improve MLIP robustness, especially for rare events.
    3. Advanced sampling methods that improve MLIP efficiency in handling diverse materials and molecular configurations.
  3. Applications
    1. In-depth studies demonstrating MLIPs in real-world applications, including material design, chemical reactions, and biological molecule simulations.
    2. Novel applications where MLIPs offer unique insights beyond traditional potential models or ab initio methods.
machine learning

Editors

The Collection will publish original research papers, and articles in various formats (full details on content types can be found here). Papers will be published in npj Computational Materials as soon as they are accepted and then collected together and promoted on the Collection homepage. All Collections are associated with a call for papers and are managed by one or more journal editors and/or Guest Editors.

This Collection welcomes submissions from all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of npj Computational Materials more generally. All submissions are subject to the same peer review process and editorial standards as regular npj Computational Materials articles, including the journal’s policy on competing interests. The Editors declare no competing interests with the submissions which they have handled through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editor who has no competing interests. For more information, refer to our Collections guidelines.

This Collection is not supported by sponsorship.