@article{TEXTUAL,
      recid = {13482},
      author = {Sahrmann, Patrick G. and Loose, Timothy D. and Durumeric,  Aleksander E. P. and Voth, Gregory A.},
      title = {Utilizing Machine Learning to Greatly Expand the Range and  Accuracy of Bottom-Up Coarse-Grained Models through Virtual  Particles},
      journal = {Journal of Chemical Theory and Computation},
      address = {2023-02-20},
      number = {TEXTUAL},
      abstract = {Coarse-grained (CG) models parametrized using atomistic  reference data, i.e., "bottom up" CG models, have proven  useful in the study of biomolecules and other soft matter.  However, the construction of highly accurate, low  resolution CG models of biomolecules remains challenging.  We demonstrate in this work how virtual particles, CG sites  with no atomistic correspondence, can be incorporated into  CG models within the context of relative entropy  minimization (REM) as latent variables. The methodology  presented, variational derivative relative entropy  minimization (VD-REM), enables optimization of virtual  particle interactions through a gradient descent algorithm  aided by machine learning. We apply this methodology to the  challenging case of a solvent-free CG model of a  1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid  bilayer and demonstrate that introduction of virtual  particles captures solvent-mediated behavior and  higher-order correlations which REM alone cannot capture in  a more standard CG model based only on the mapping of  collections of atoms to the CG sites.},
      url = {http://knowledge.uchicago.edu/record/13482},
}