  System specific neural force fields (NFFs) have gained popularity in
computational chemistry. One of the most popular datasets as a bencharmk to
develop NFFs models is the MD17 dataset and its subsequent extension. These
datasets comprise geometries from the equilibrium region of the ground
electronic state potential energy surface, sampled from direct adiabatic
dynamics. However, many chemical reactions involve significant molecular
geometrical deformations, for example, bond breaking. Therefore, MD17 is
inadequate to represent a chemical reaction. To address this limitation in
MD17, we introduce a new dataset, called Extended Excited-state Molecular
Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from
direct non-adiabatic dynamics, and the energies are computed at both
multireference wavefunction theory and density functional theory. We show that
the xxMD dataset involves diverse geometries which represent chemical
reactions. Assessment of NFF models on xxMD dataset reveals significantly
higher predictive errors than those reported for MD17 and its variants. This
work underscores the challenges faced in crafting a generalizable NFF model
with extrapolation capability.
