The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive and leading-edge venue for research and discussions at the interface of machine learning (ML) and the physical sciences. This interface spans (1) applications of ML in physical sciences (ML for physics), (2) developments in ML motivated by physical insights (physics for ML), and most recently (3) convergence of ML and physical sciences (physics with ML) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.

Recent years have seen a tremendous increase in cases where ML models are used for scientific processing and discovery, and similarly, instances where tools and insights from the physical sciences are brought to the study of ML models. The harmonious co-development of the two fields is not a surprise: ML methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Indeed, in some sense, ML and physics are concerned with a shared goal of characterizing the true probability distributions of nature. As ML and physical science research becomes more intertwined, questions naturally arise around what scientific understanding is when science is performed with the assistance of complex and highly parameterized models.

The breadth of work at the intersection of ML and physical sciences is answering many important questions for both fields while opening up new ones that can only be addressed by a joint effort of both communities. By bringing together ML researchers and physical scientists who apply and study ML, we expect to strengthen the much needed interdisciplinary dialogue, introduce exciting new open problems to the broader community, and stimulate the production of new approaches to solving challenging open problems in the sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop, which will also include contributed talks selected from submissions.

A part of the workshop program will be dedicated to a focus area discussing a concrete open question of interest to the community: the role of inductive bias and interpretability in PS with ML, and the role of physically-informed inductive biases in ML models. This critical and ongoing conversation because it gets to the heart of what it means to do science in the era of deep learning. The program will also feature a moderated panel discussion on "Funding and Institutional Support for Machine Learning and Physical Sciences Research" -- a topic of urgent current interest to researchers as the nascent field is rallying for institutional support across university departments, national labs, government-funded AI institutes, and industry. The goal of the panel will be to elucidate for researchers across many career stages the various support mechanisms available for this intersectional field and future prospects.

NeurIPS 2023

The Machine Learning and the Physical Sciences 2023 workshop will be held on December 15, 2023 at the New Orleans Convention Center in New Orleans, USA as a part of the 37th annual conference on Neural Information Processing Systems (NeurIPS). The workshop is planned to take place in a hybrid format inclusive of virtual participation.


Plenary speakers and focus area panelists.


Topic: Funding and institutional support for research at the intersection of machine learning and physical sciences.

Call for papers

In this workshop, we aim to bring 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, materials science, biophysics, and related sciences; and
  • Using physical insights to understand and/or improve machine learning techniques.
In this year's workshop, there are two (2) tracks:

Research Track

We invite researchers to submit original work in the following areas or areas related to them.
  • ML for Physics: Innovative applications of machine learning to the physical sciences; Machine learning model interpretability for obtaining insights to 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.
  • 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, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.

Dataset Track

We invite researchers to submit papers describing a dataset and the related computational or scientific challenge that may benefit from innovative research at the intersection of ML and Physical Sciences.
  • Availability of data set: The dataset must be publicly available at the time of the workshop (e.g., via Zenodo). The submission must also include a baseline result along with public code (the baseline need not use machine learning). Additional data artifacts (e.g. a public simulator) may also be included and described in the submission.
  • The submitted paper should describe the the following:
    • Properties of the dataset.
    • The scientific and/or computational challenges.
    • Existing methods and/or potential solutions that could be provided by ML.

Submissions should be anonymized short papers (extended abstracts) up to 4 pages in length (excluding references). We invite authors to follow the guidelines and best practices from the NeurIPS conference (see also the main conference datasets and benchmarks call for guidelines pertaining to the Dataset Track). Please ensure that your paper is approachable by someone who is not an expert in your specific area of physical science. For example, please avoid or at least define jargon. We reserve the right to desk reject any submissions that do not conform to the format. The review process is double blind. All authors must be registered in the submission system at the time of submission. We will not allow authors to be added after the review process has begun. This workshop is not archival so we will consider papers containing content that is published in an archival venue other than the main NeurIPS conference (e.g. a physics journal). However, such papers will likely need to be rewritten to fit the format and venue. See here for additional instructions on preparing submissions to the workshop.

Posters and contributed talks

Accepted work will be presented as posters during the workshop. At the same time as the in-person poster session, we will also facilitate a virtual poster session in GatherTown. Authors of submitted papers will be able to indicate their preference for an in-person presentation or a virtual presentation. Furthermore, the authors of each accepted paper will get the opportunity to submit a 5 minute video that summarizes their work.

Several accepted submissions will be selected for contributed talks at the workshop program. Talks can be in-person or remote depending on the preference of the presenter.

Submission instructions

Submit your work on the submission portal. See submission and review instructions for important instructions on preparing contributions as well as details on how they will be evaluated.

Submit paper

Review instructions and call for reviewers

Instructions for reviewers are available here.

Review instruction

We would appreciate your skills as a reviewer! Please consider applying for that role:

Sign up to review

Important note for work that will be/has been published elsewhere

All accepted works will be made available on the workshop website. This does not constitute an archival publication or formal proceedings; authors retain full copyright of their work and are free to publish their extended work in another journal or conference. We allow submission of works that overlap with papers that are under review or have been recently published in a conference or a journal, including physical science journals. However, we do not accept cross-submissions of the same content to multiple workshops at NeurIPS. (Check the list of accepted workshops this year).

Important dates

  • Submission Deadline: September 29, 2023, 23:59 AoE
  • Review Deadline: October 21, 2023, 23:59 AoE
  • Author (accept/reject) notification: October 27, 2023, 23:59 AoE
  • Workshop: December 15, 2023


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

Steering Committee



  • BIDS


Sponsors are welcome. Please contact us.


NeurIPS 2023 will be a hybrid conference with physical and virtual participation. The physical component will take place at the New Orleans Ernest N. Morial Convention Center, 900 Convention Center Blvd, New Orleans, LA 70130, United States