APL Machine Learning features vibrant and timely research for two 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 also considers research that substantially describes quantitative models and theories, especially if the research is validated with experimental results.
Graphic showing the difference of machine learning for physical sciences and applied physical sciences for better machine learning systems.

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
  • ML-led techniques and approaches for applications in physics, engineering, chemistry, biology, pharmacy, medicine
  • Novel frameworks for data-driven or ML-led approaches for discovery of novel materials & molecules (or novel properties)
  • ML-led techniques in atomistic/molecular simulations
  • Data-driven empirical models
  • ML-led high-throughput techniques
  • Experimental studies for validation of ML-led models
  • Platforms, tools and infrastructure enabling machine learning for applied physics

AP (applied physics) for ML (machine learning):

  • Materials, devices and systems for ML/AI accelerators
  • Neuromorphic materials, devices and systems
  • Materials, devices and systems for non-Von Neumann and unconventional computing
  • Multi-modal (electrical, optical, thermal) intelligent materials, devices and systems
  • Brain-inspired artificial systems
  • Energy-efficient and computationally efficient AI/ML systems

Open Access Statement

APL Machine Learning is an open access journal. Articles published in APL Machine Learning are freely accessible, without restrictions, to the global public. Authors who wish to publish in APL Machine Learning retain the copyright to their work under a Creative Commons license. Under this license, users are free to share and adapt the material in any format, provided appropriate credit is given. Visit the AIP Publishing Open Access Policy for more information about our policies on open access.

ISSN: 2770-9019