The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.

By W. Noid, J. Chu, G. Ayton, Vinod Krishna, S. Izvekov, Gregory A. Voth, Avisek Das, H. C. Andersen. Journal of Chemical Physics, 2008

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The power of coarse graining in biomolecular simulations (2013, methodology): “FM 7,8 is another popular technique for constructing CG potentials.”
Machine learning for molecular simulation (2019, methodology): “ has also been used in combination with enhanced sampling methods to learn the free energy surface on-they [26, 60, 61, 22, 62, 63, 64, 65]. An alternative way to learn (6) is by using force matching [66, 67]. It has been demonstrated [68] that, given the forces r xUof the atomistic system collected”
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network (2018, methodology): “It is common practice in model reduction to use a function approximator to express the unknown terms in the reduced models, such as approximating subgrid stress for large-eddy simulation[27, 28, 29] or approximating interatomic forces for coarse-grained molecular dynamic systems[30, 31].”
Challenges in Multiscale Modeling of Polymer Dynamics (2013, neutral): “[171–173].”
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, methodology): “duced a practical approach to the optimisation of G, termed the “Multiscale CoarseGraining/Force Matching” (MSCG/FM) method [91, 109].”