A dataset of chemical reaction pathways incorporating halogen chemistry.

Machine learning interatomic potentials (MLIPs) promise to revolutionize computational chemistry; however, their performance depends critically on the quality and diversity of the training data. Existing quantum chemical datasets predominantly focus on equilibrium structures and exhibit limited halogen coverage, despite halogens being present in approximately 25% of pharmaceuticals and numerous materials. We present Halo8, a comprehensive dataset that addresses this gap by systematically incorporating fluorine, chlorine, and bromine chemistry into reaction pathway sampling. Using our efficient multi-level computational workflow, which achieves a 110-fold speedup over pure DFT approaches, Halo8 comprises approximately 20 million quantum chemical calculations from 19,000 unique reaction pathways. The dataset combines recalculated Transition1x reactions with new halogen-containing molecules from GDB-13, employing systematic halogen substitution to maximize chemical diversity. All calculations were performed at the ωB97X-3c level, providing accurate energies, forces, dipole moments, and partial charges. Validation demonstrates that Halo8 captures diverse structural distortions and chemical environments essential for reactive systems, serving as a valuable resource for training MLIPs applicable to pharmaceutical discovery, materials design, and catalysis.