About

Since its inception in 2017, the Machine Learning and the Physical Sciences (ML4PS) workshop has served as a unique gathering space for the growing community spearheading cross-cutting research topics at the intersection of machine learning (ML) and the physical sciences (PS). This includes the applications of ML to problems in the physical sciences (ML for PS) as well as developments in ML motivated by physical insights (PS for ML). The physical sciences are defined inclusively, including but not limited to physics, astronomy, cosmology, chemistry, biophysics, materials science, and Earth science.

The physical sciences pose challenging, high-profile questions that invite innovation and opportunities for cross-pollination between ML researchers and physical scientists. Recent years have seen a tremendous increase in cases where ML models are used for scientific inference and discovery (e.g., geometric deep learning, simulation-based inference), and simultaneously tools and insights from the physical sciences have been used to develop efficient ML models (e.g., diffusion models and physics-informed neural networks). The communities coalescing at this workshop are encouraged to pursue fresh, synergistic solutions to "big science" questions that can spark new approaches in ML.

This year's programming explores the evolving interplay between academia and industry in basic research. Invited talks and panel discussions emphasize the myriad foundational and translational connections between these domains. Furthermore, we ask: how can our community help sustain open, curiosity-driven research in physics and related sciences when traditional funding models are under strain? What role can industry play in supporting fundamental science, and how can the insights emerging from basic research in the physical sciences and ML help catalyze wider innovations in industry and beyond?

NeurIPS 2025

The Machine Learning and the Physical Sciences 2025 workshop will be held on December 6 or 7, 2025 at the San Diego Convention Center in San Diego, Californa (USA) as a part of the 39th annual conference on Neural Information Processing Systems (NeurIPS). The workshop is planned to take place in a hybrid format inclusive of virtual participation.

Tentative Schedule

All times are local, i.e. Pacific Standard Time (PST).

8:15am - 8:30am Opening remarks
8:30am - 10:00am Invited talks: Foundational connections between ML & the physical sciences
10:00am - 10:30am Coffee break
10:30am - 11:00am Paper spotlight talks
11:00am - 12:00pm Poster session #1
12:00pm - 1:00pm Lunch break
1:00pm - 2:00pm Panel Discussion: How should we translate ML advances between academia & industry?
Exploring the interplay of academia & industry using weather and climate research as a case study.
2:00pm - 2:30pm Paper spotlight talks
2:30pm - 3:30pm Poster session #2
3:30pm - 4:00pm Coffee break
4:00pm - 5:00pm Panel Discussion: What could the "AlphaFold moment" be for fundamental physics?
A conversation on paradigm-shifting applications across ML and particle/astro/accelerator physics.
5:00pm - 5:15pm Closing remarks

Confirmed Speakers

Confirmed Panelists

Call for papers

This workshop brings together physical scientists and machine learning researchers who work at the intersection of these fields by:

  • applying machine learning to problems in the physical sciences -- physics, chemistry, astronomy, earth science, biophysics, and related sciences; and
  • using physical insights to understand and/or improve machine learning techniques, for instance building hybrid machine learning algorithms that leverage physical models with machine learning blocks to create interpretable and accurate predictive models.

Accepted contributions will be presented during in-person poster sessions during the workshop. Authors will also have the opportunity to upload an optional short video summary alongside their accepted paper on our website. Selected contributions will be offered 15-minute spotlight talks. We will highlight a small number of submissions containing exceptionally well-documented code and reproducible workflows with a Reproducibility Badge.

Sign up to volunteer as a reviewer: Our community’s success relies on our reviewers – we need you! Please help us ensure a high quality program by registering to review a few papers. Follow this link to nominate yourself and let us know how many papers you would be able to review.

Important dates

  • Submission Deadline: Friday, Aug 29, 2025, 23:59 AoE
  • Author (accept/reject) notification: Friday, Sept 22, 2025, 23:59 AoE
  • Workshop: December 6 or 7, 2025

Submission tracks

    1. Research: We invite contributions on either completed or high-quality work-in-progress original research in the following areas:

    • ML for Physics: Innovative applications of machine learning to the physical sciences; Machine learning model interpretability for obtaining insights into physical systems; Automating/accelerating elements of the scientific process (experimental design, data collection, statistical analysis, etc.).
    • Physics in ML: Strategies for incorporating scientific knowledge or methods into machine learning models and algorithms; Applications of physical science methods and processes to understand, model, and improve machine learning models and algorithms.
    • Other areas: Any other area related to the subject of the workshop, including but not limited to probabilistic methods that are relevant to physical systems, such as deep generative models, scientific foundation models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.
    2. Datasets & Benchmarks: We invite contributions describing a dataset and/or corresponding benchmarks at the intersection of ML and Physical Sciences, in particular showcasing the unique nature of physical datasets and forward models in the context of ML applications.

    3. Perspectives: We invite researchers to present compelling and grounded viewpoints on recent directions and open questions at the intersection of ML and Physical Sciences. This track encourages perspectives on past, present, or future challenges that can stimulate productive and respectful conversations on timely topics that will benefit from the ML4PS workshop's attendees' input. Papers should meet standard scientific rigor, including using evidence and reasoning to support claims, including relevant background and context, and attributing others' work via appropriate citations.

Submission instructions

Submissions should be short papers up to 4 pages in length (excluding references).

Full submission guidelines

Organizers

For questions and comments, please contact us at ml4ps@googlegroups.com.

Steering Committee

Sponsors

Location

NeurIPS 2025 will take place at the San Diego Convention Center, 111 Harbor Dr, San Diego, CA 92101, United States.