CERN and Mistral Partner to Develop AI Foundation Models for Scientific Discovery

CERN and Mistral Form Strategic AI Partnership

CERN, the European Laboratory for Particle Physics, and Mistral, a European developer of large-scale AI models, have signed a statement of intent. This agreement establishes a long-term strategic partnership aimed at developing foundation models for scientific discovery.

Advancing AI for Science

The collaboration combines CERN’s expertise in fundamental physics, scientific data, and large-scale computing with Mistral’s capabilities in large-scale model training. This joint effort is intended to advance AI for science, initially focusing on high-energy physics. The partnership also has the potential to contribute AI approaches to the broader scientific community.

Developing a Large Physics Model

A core objective is to develop a single pretrained Large Physics Model, which will be the first foundation model for collider physics. This model will be trained using billions of simulated proton-proton collisions from CERN’s Large Hadron Collider (LHC). Its design supports multiple tasks, including reconstructing particle collisions, identifying particles, flagging unexpected signals, and evaluating future detector designs.

Addressing Challenges in LHC Data Analysis

The Large Physics Model is intended to address a fundamental challenge in analyzing LHC collisions. Current reconstruction methods rely on specialized algorithms. While machine learning has improved performance in specific tasks, foundation models offer the potential to learn across the entire reconstruction chain and support multiple tasks within a single model, building on existing advances.

CERN's Data Volume and AI Development

CERN houses a significant volume of scientific data, with approximately one exabyte of experimental data from the LHC stored to date. Enrica Porcari, CERN’s Chief Information Officer, stated that high-energy physics provides a unique environment for developing and rigorously testing artificial intelligence, with the goal of advancing AI capabilities for the entire physics community.