[CFP] ICLR 2026 Workshop: Foundation Models for Science + Paper Prize Awards

[Wuyang Chen]

Working on foundation models for scientific problems? Consider submitting your paper to the 2nd Workshop on Foundation Models for Science at ICLR 2026!

Submission details: https://fm-science.github.io/cfp.html
OpenReview submission portal:https://openreview.net/group?id=ICLR.cc/2026/Workshop/FM4Science

Tentative important dates (AoE time):

• Abstract Submission Deadline: February 8, 2026
• Paper Submission Deadline: February 10, 2026
• Review Bidding Period: February 8 - February 11, 2026
• Review Deadline: February 24, 2026
• Acceptance/Rejection Notification Date: March 1, 2026
• Camera-Ready Submission: April 1, 2026
• Workshop Date: April 26 or 27, 2026

This year we have two great sponsors, NERSC (https://www.nersc.gov/) and Terraferma (https://www.terraferma.ai/), for their generous support of the paper awards for winners and runners-up! We look forward to announcing additional sponsors soon.

Topics: include but are not limited to:

• How to achieve better scaling laws for foundation models in scientific problems by designing datasets, network architectures, and training algorithms?
• How to design data augmentation and multi-modal self-supervised pretraining for scientific problems?
• How to design efficient fine-tuning with scientific awareness?
• How to quantify and reduce the uncertainty of scientific foundation models?
• How to improve the out-of-distribution generalization of scientific foundation models?
• How to make foundation models compatible with and enable the integration of classic scientific tools (simulators, solvers, etc.)?
• How toientific foundation models benefit from the reasoning and in-context learning of LLMs?
• How to better combine symbolic learning and data-driven learning?
• How to use foundation models to facilitate visualizations in scientific problems?
• How to accelerate scientific discovery and the collection/assimilation of scientific data with foundation models?
• How to diagnose failure cases or modes where scientific foundation models do not perform well?
• How to align scientific foundation models with scientific facts without hallucination?

Scientific Domains. We invite paper submissions from various scientific domains, including but not limited to: Astrophysics and Space Science, Biomedicine (e.g., proteins, biosequences, virtual screening), Computational Science (e.g., PDEs, forecasting),Earth Science, Materials Science (e.g., batteries, chemical synthesis), Quantum Mechanics (e.g., nuclear fusion), Small Molecules. Applications-driven submissions focusing on AI-for-Science and Scientific Machine Learning (SciML) are also highly encouraged.

For other information about our workshop, please visit: https://fm-science.github.io/index.html

If you have any questions about paper submission and the workshop, please send email to: foundationm...@gmail.com

We look forward to your contributions!

Sincerely,

Wuyang
--
Wuyang Chen, Ph.D.
Assistant Professor of Computing Science
Simon Fraser University, Burnaby, BC, Canada
Pronouns: he/him/his
https://delta-lab-ai.github.io/
https://www.sfu.ca/fas/computing/people/faculty/faculty-members/wuyang-chen.html