OMat24 is a series of model checkpoints released by Meta's FAIR Chemistry team, differing in model size and training strategies. These models employ the EquiformerV2 architecture to advance research in materials science by predicting material properties through machine learning models, thereby accelerating the discovery and development of new materials. The models have been pretrained on public datasets and are available in various scales to meet different research needs.