Name: ANI-1xnr Extended ID: ANI-1xnr__Zhang-Makos-Jadrich-Kraka-Barros-Nebgen-Tretiak-Isayev-Lubbers-Messerly-Smith__DS_elj5dyxwg3mw_0 Description: ANI-1xnr was developed to train the ANI-1xnr model, intended to model reactive chemistry. Specifically, ANI-1xnr is meant to represent carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. The dataset was generated using an active learning method in which ab initio nanoreactor simulations supplied MLIP training; the MLIP was subsequently tested and new simulations were generated based on structures tested with high uncertainty to supply the next cycle of MLIP training. Authors: Shuhao Zhang Małgorzata Z. Makoś Ryan B. Jadrich Elfi Kraka Kipton Barros Benjamin T. Nebgen Sergei Tretiak Olexandr Isayev Nicholas Lubbers Richard A. Messerly Justin S. Smith DOI: 10.60732/ad56ac0a Calculated Property Types: atomic_forces energy Elements: C (20.46%) H (31.97%) N (24.45%) O (23.13%) Methods: KS-DFT-BLYP+D3 Software: CP2K Number of Configurations: 196,550 Number of Atoms: 27,209,270 Publication Link: https://doi.org/10.1038/s41557-023-01427-3 Data Source Link: https://doi.org/10.6084/m9.figshare.22814579