LLNL, Meta Create Cutting-Edge Polymer AI Dataset

New open dataset aims to accelerate materials discovery with AI

Published on Mar. 6, 2026

Got story updates? Submit your updates here. ›

Researchers from Lawrence Livermore National Laboratory (LLNL) and Meta have created the world's largest open dataset of atomistic polymer chemistry - a trove of millions of quantum-accurate simulations designed to help AI model the complex behavior of plastics, films, batteries and countless everyday materials. The OPoly26 dataset contains more than 6 million density functional theory (DFT) calculations on polymeric chemical systems, making it nearly ten times larger than the next largest comparable polymer dataset.

Why it matters

Advances in polymer science open pathways for recycling and upcycling waste materials into more valuable chemical feedstocks. Many widely used polymers are also Per- and Polyfluoroalkyl Substances (PFAS), widely recognized as "forever chemicals" with significant environmental impact. This new dataset aims to accelerate AI-driven materials discovery to address these critical challenges.

The details

The partnership between LLNL and Meta seeks to address the longstanding gap in polymer data by generating critical missing data on polymers with the shared goals of expanding and democratizing open datasets for materials scientists. LLNL contributed significant computational power and polymer domain knowledge, while Meta contributed vast computational resources to perform 1.2 billion core hours of DFT simulations and train state-of-the-art machine-learned interatomic potential (MLIP) models.

  • The OPoly26 dataset was created in 2026.

The players

Lawrence Livermore National Laboratory (LLNL)

A U.S. Department of Energy national laboratory that specializes in research and development in national security, energy, and the environment.

Meta

A technology company that develops social media applications and other digital products.

Evan Antoniuk

An LLNL materials scientist and co-principal investigator of the OPoly26 dataset.

Nick Liesen

An LLNL staff scientist who worked on the OPoly26 dataset.

Sam Blau

A Lawrence Berkeley National Laboratory chemist and co-principal investigator of the OPoly26 dataset.

Got photos? Submit your photos here. ›

What they’re saying

“This fills a huge gap. We see this as a community resource, one that we hope becomes the go-to starting point for anyone interested in performing atomistic simulations of polymers.”

— Evan Antoniuk, LLNL materials scientist and OPoly26 co-principal investigator

“Meta's partnership with LLNL demonstrates how open science and AI can accelerate breakthroughs in materials research. By making this dataset publicly available, we're giving scientists potent new tools to address critical challenges in healthcare and beyond.”

— Rob Sherman, Vice president of policy at Meta

“Reactivity - the breakage and formation of chemical bonds - is central to polymer synthesis, manufacturing, aging and recycling, and to nanoscale patterning of polymer thin films for semiconductor manufacturing. By going beyond stable structures and explicitly sampling hundreds of thousands of reactive configurations, we aim to accurately describe the reactive events that often govern polymer behavior under real-world conditions.”

— Sam Blau, LBNL chemist and OPoly26 co-principal investigator

What’s next

The team plans to continue evaluating the MLIP models against experimental measurements to gauge how well they can capture real-world polymer properties.

The takeaway

This new open dataset represents a significant advancement in materials science, providing researchers with a powerful tool to accelerate the discovery and development of innovative polymers that can address critical environmental and sustainability challenges.