In a landmark collaboration between Meta and Lawrence Livermore National Laboratory (LLNL), researchers have unveiled OPoly26, the world's largest open dataset dedicated to atomistic polymer chemistry. This comprehensive resource contains over 6 million density functional theory (DFT) calculations, offering quantum-accurate simulations of millions of polymer structures. With a volume nearly ten times larger than any comparable dataset, OPoly26 aims to close a critical data gap and accelerate the development of safer, more sustainable materials through artificial intelligence. Polymers are integral to modern life, found in everything from clothing and packaging to electronics and transportation infrastructure. However, the scientific community has long faced a scarcity of high-quality data required to model complex polymer behaviors, particularly regarding recycling, upcycling, and the degradation of persistent pollutants like PFAS (forever chemicals). OPoly26 addresses this by providing a massive reference library that enables AI models to learn patterns from pre-computed structures in hours or days, rather than years. The project builds upon the success of Open Molecules 2025 (OMol25), a similar initiative led by Meta and Lawrence Berkeley National Laboratory. By combining LLNL's computational supremacy with Meta's expertise in machine learning, the partnership has compressed years of simulation work into months. LLNL contributed significant domain knowledge and access to Tuolumne, the laboratory's powerful supercomputer, which facilitated the generation of diverse polymer structures. Meanwhile, Meta dedicated 1.2 billion core hours to perform the DFT simulations and train state-of-the-art machine-learned interatomic potentials (MLIPs). "This fills a huge gap," said Evan Antoniuk, a materials scientist at LLNL and co-principal investigator. "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." The research highlights the importance of capturing reactive configurations—instances where chemical bonds break or form—essential for understanding polymer synthesis, aging, and recycling. Unlike previous datasets that focused primarily on stable structures, OPoly26 explicitly samples hundreds of thousands of reactive scenarios. This approach ensures that AI models can accurately describe both reactive and nonreactive behaviors under realistic conditions. The team demonstrated that incorporating polymer-specific data alongside small-molecule training sets substantially improves model accuracy. Rob Sherman, Meta's Vice President of Policy, emphasized the broader impact of the initiative: "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 health care and beyond." To ensure the data remains a lasting asset, all information is being released under an open license to maximize reuse and reproducibility. The study, published on the arXiv preprint server, also introduces a suite of polymer-specific evaluation tasks to benchmark how well AI models capture phenomena such as polymer solvation. Future efforts will involve validating these models against experimental measurements to gauge their performance in real-world applications. Through this public-private collaboration, the team hopes to democratize access to high-fidelity materials data, fueling advancements across academia, industry, and government. As Ibo Matthews, LLNL Materials Science Division Leader, noted, the scale of this dataset is essential not only for generating data but for rigorously evaluating how well AI models can predict the full range of polymer behaviors relevant to future technologies.