Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Editorial: Publishing Physical Sciences in the Era of AI

Michele Ceriotti1, Mario Krenn2, Ann B. Lee3, Nicola Marzari4, Benjamin Nachman5, Mariel Pettee6, and O. Anatole von Lilienfeld7,8,*

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • 2Department of Computer Science, University of Tübingen, Tübingen, Germany
  • 3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
  • 4Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, United Kingdom
  • 5Department of Particle Physics and Astrophysics, Stanford University and SLAC National Laboratory, Stanford, California, USA
  • 6Department of Physics, University of Wisconsin–Madison, Madison, Wisconsin, USA
  • 7Clark Chair in Advanced Materials, Department of Chemistry, Department of Materials Science & Engineering, and Department of Physics, University of Toronto, Toronto, Ontario, Canada
  • 8Canada CIFAR AI Chair, Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

  • *Contact author: lilienfeld@aps.org

PRX Intelligence 1, 010001 – Published 28 July, 2026

DOI: https://doi.org/10.1103/PRXINTELL.1.010001

Abstract

We present the vision, scope, and editorial philosophy of PRX Intelligence, a selective, fully open access journal at the intersection of artificial intelligence, machine learning, and the physical sciences. We reflect on the transformational role of artificial intelligence as an emerging paradigm of science, examine its implications for scientific understanding and practice, and outline editorial standards that will guide this venue as a home for rigorous and impactful research.

View figure in article

Article Text

References (40)

  1. R. Van Noorden, B. Maher, and R. Nuzzo, The top 100 papers, Nature (London) 514, 550 (2014).
  2. R. Van Noorden, These are the most-cited research papers of all time, Nature (London) 640, 591 (2025).
  3. T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research, Redmond, 2009).
  4. The Nobel Prize in Physics (2024), awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
  5. The Nobel Prize in Chemistry (2024), awarded to David Baker, Demis Hassabis, and John Jumper “for computational protein design and protein structure prediction.”
  6. R. S. Sutton, The bitter lesson (2019).
  7. C. Lu, C. Lu, R. T. Lange, Y. Yamada, S. Hu, J. Foerster, D. Ha, and J. Clune, Towards end-to-end automation of AI research, Nature (London) 651, 914 (2026).
  8. AutoResearchClaw, Autoresearchclaw.
  9. A. Karpathy, Autoresearch.
  10. R. D. King, J. Rowland, S. G. Oliver, M. Young, W. Aubrey, E. Byrne, M. Liakata, M. Markham, P. Pir, L. N. Soldatova, A. Sparkes, K. E. Whelan, and A. Clare, The automation of science, Science 324, 85 (2009).
  11. D. A. Boiko, R. MacKnight, B. Kline, and G. Gomes, Autonomous chemical research with large language models, Nature (London) 624, 570 (2023).
  12. M. Krenn, R. Pollice, S. Y. Guo, M. Aldeghi, A. Cervera-Lierta, P. Friederich, G. dos Passos Gomes, F. Häse, A. Jinich, A. Nigam, Z. Yao, and A. Aspuru-Guzik, On scientific understanding with artificial intelligence, Nat. Rev. Phys. 4, 761 (2022).
  13. D. Breunig, Does the bitter lesson have limits? (2025).
  14. K. Cranmer, The bittersweet lesson (2025).
  15. K. Kusumegi, X. Yang, P. Ginsparg, M. de Vaan, T. Stuart, and Y. Yin, Scientific production in the era of large language models, Science 390, 1240 (2025).
  16. Q. Hao, F. Xu, Y. Li, and J. Evans, Artificial intelligence tools expand scientists' impact but contract science's focus, Nature (London) 649, 1237 (2026).
  17. G. Channing and A. Ghosh, AI for scientific discovery is a social problem, Patterns 7, 101497 (2026).
  18. C. A. E. Goodhart, Monetary Theory and Practice (Macmillan, London, 1984).
  19. The White House and U.S. Department of Energy, Launching the Genesis Mission, Executive Order (November 24, 2025); U.S. Department of Energy, Energy department advances investments in AI for science (December 10, 2025).
  20. European Commission, Raise: Resource for AI science in Europe (2025).
  21. M. Abolhasani and E. Kumacheva, The rise of self-driving labs in chemical and materials sciences, Nat. Synth. 2, 483 (2023).
  22. American Physical Society, Appropriate use of AI tools (2026).
  23. Lord Kelvin (W. Thomson), Popular Lectures and Addresses, Vol. 1 (Macmillan, London, 1889).
  24. R. P. Feynman, Seeking new laws, in The Character of Physical Law (MIT Press, Cambridge, MA, 1965), Chap. 7.
  25. H. M. Nussenzveig, Introduction to Quantum Optics, Documents on Modern Physics (Gordon and Breach, London and New York, 1973).
  26. R. M. Nieminen, From number crunching to virtual reality, in Mathematics Unlimited—2001 and Beyond, edited by B. Engquist and W. Schmid (Springer, Berlin, 2001), pp. 937–960.
  27. O. A. von Lilienfeld, Quantum machine learning in chemical compound space, Angew. Chem. Int. Ed. 57, 4164 (2018).
  28. S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K.-R. Müller, Unmasking Clever Hans predictors and assessing what machines really learn, Nat. Commun. 10, 1096 (2019).
  29. J. Kauffmann, J. Dippel, L. Ruff, W. Samek, K.-R. Müller, and G. Montavon, Explainable AI reveals Clever Hans effects in unsupervised learning models, Nat. Mach. Intell. 7, 412 (2025).
  30. P. A. M. Dirac, Quantum mechanics of many-electron systems, Proc. R. Soc. London A 123, 714 (1929).
  31. W. Yu, E. Abdelaleem, I. Nemenman, and J. C. Burton, Physics-tailored machine learning reveals unexpected physics in dusty plasmas, Proc. Natl. Acad. Sci. USA 122, e2505725122 (2025).
  32. E. Strubell, A. Ganesh, and A. McCallum, Energy and policy considerations for deep learning in NLP, in Proc. 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, 2019), pp. 3645–3650.
  33. F. Musil, A. Grisafi, A. P. Bartók, C. Ortner, G. Csányi, and M. Ceriotti, Physics-inspired structural representations for molecules and materials, Chem. Rev. 121, 9759 (2021).
  34. M. Krenn, M. Erhard, and A. Zeilinger, Computer-inspired quantum experiments, Nat. Rev. Phys. 2, 649 (2020).
  35. N. Dalmasso, L. Masserano, D. Zhao, R. Izbicki, and A. B. Lee, Likelihood-free frequentist inference: Bridging classical statistics and machine learning for reliable simulator-based inference, Electron. J. Stat. 18, 5045 (2024).
  36. L. Talirz, S. Kumbhar, E. Passaro, A. V. Yakutovich, V. Granata, F. Gargiulo, M. Borelli, M. Uhrin, S. P. Huber, S. Zoupanos, C. S. Adorf, C. W. Andersen, O. Schütt, C. A. Pignedoli, D. Passerone, J. VandeVondele, T. C. Schulthess, B. Smit, G. Pizzi, and N. Marzari, Materials cloud, a platform for open computational science, Sci. Data 7, 299 (2020).
  37. G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, and D. Shih, Machine learning in the search for new fundamental physics, Nat. Rev. Phys. 4, 399 (2022).
  38. J. McCabe, B. Régaldo-Saint Blancard, L. Parker, R. Ohana, M. Cranmer, A. Bietti, M. Eickenberg, S. Golkar, G. Krawezik, F. Lanusse, M. Pettee, T. Tesileanu, K. Cho, and S. Ho, Multiple physics pretraining for physical surrogate models, in NeurIPS 2023 AI for Science Workshop (NeurIPS, 2023).
  39. O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, Exploring chemical compound space with quantum-based machine learning, Nat. Rev. Chem. 4, 347 (2020).
  40. M. A. Edwards and S. Roy, Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition, Environ. Eng. Sci. 34, 51 (2017).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation