@article{TEXTUAL,
      recid = {11131},
      author = {Pengmei, Zihan and Liu, Junyu and Shu, Yinan},
      title = {Beyond MD17: The reactive xxMD dataset},
      journal = {Scientific Data},
      address = {2024-02-20},
      number = {TEXTUAL},
      abstract = {System specific neural force fields (NFFs) have gained  popularity in computational chemistry. One of the most  popular datasets as a bencharmk to develop NFF 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 nonadiabatic 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.},
      url = {http://knowledge.uchicago.edu/record/11131},
}