Assessing the persistence of chalcogen bonds in solution with neural network potentials.

@article{Jurskov2022AssessingTP,
  title={Assessing the persistence of chalcogen bonds in solution with neural network potentials.},
  author={Veronika Jur{\'a}skov{\'a} and Fr{\'e}d{\'e}ric C{\'e}lerse and Rub{\'e}n Laplaza and Cl{\'e}mence Corminboeuf},
  journal={The Journal of chemical physics},
  year={2022},
  volume={156 15},
  pages={
          154112
        }
}
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular… 
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