The American Physical Society’s newest highly selective, open access journal, PRX Intelligence, has published its inaugural papers. The studies demonstrate how artificial intelligence and machine learning methods can be used to advance scientific knowledge and capabilities across the physical sciences — from neural networks that streamline molecular simulations to data-driven learning schemes that accelerate quantum embedding workflows.

As AI and machine learning transform the physical sciences, PRX Intelligence is designed to provide a multidisciplinary platform for research pioneering the development and application of these approaches. Building on the foundation of Physical Review X, the journal publishes open access articles expected to have substantial and lasting impact. It welcomes studies that apply AI and machine learning across theory, simulation, and experimentation in physics and related fields — including computer science, mathematics, engineering, materials science, chemistry, biology, and earth and environmental sciences. Relevant topics include discovery and synthesis, physics-informed learning, data-driven approaches, machine learning pipelines for observational platforms, and more. Articles have flexible formats and lengths and may include research papers, perspectives, roadmaps, tutorials, and more.

PRX Intelligence will waive article publication charges for manuscripts submitted or transferred before Jan. 1, 2027. And like all other APS journals, it will always waive these charges for researchers in low-and middle-income countries. Sign up for email updates to keep up with the latest news from the journal.