@article{TEXTUAL,
      recid = {5433},
      author = {Jin, Jaehyeok and Pak, Alexander J. and Durumeric,  Aleksander E. P. and Loose, Timothy D. and Voth, Gregory  A.},
      title = {Bottom-up Coarse-Graining: Principles and Perspectives},
      journal = {Journal of Chemical Theory and Computation},
      address = {2022-09-07},
      number = {TEXTUAL},
      abstract = {Large-scale computational molecular models provide  scientists a means to investigate the effect of microscopic  details on emergent mesoscopic behavior. Elucidating the  relationship between variations on the molecular scale and  macroscopic observable properties facilitates an  understanding of the molecular interactions driving the  properties of real world materials and complex systems  (e.g., those found in biology, chemistry, and materials  science). As a result, discovering an explicit, systematic  connection between microscopic nature and emergent  mesoscopic behavior is a fundamental goal for this type of  investigation. The molecular forces critical to driving the  behavior of complex heterogeneous systems are often  unclear. More problematically, simulations of  representative model systems are often prohibitively  expensive from both spatial and temporal perspectives,  impeding straightforward investigations over possible  hypotheses characterizing molecular behavior. While the  reduction in resolution of a study, such as moving from an  atomistic simulation to that of the resolution of large  coarse-grained (CG) groups of atoms, can partially  ameliorate the cost of individual simulations, the  relationship between the proposed microscopic details and  this intermediate resolution is nontrivial and presents new  obstacles to study. Small portions of these complex systems  can be realistically simulated. Alone, these smaller  simulations likely do not provide insight into collectively  emergent behavior. However, by proposing that the driving  forces in both smaller and larger systems (containing many  related copies of the smaller system) have an explicit  connection, systematic bottom-up CG techniques can be used  to transfer CG hypotheses discovered using a smaller scale  system to a larger system of primary interest. The proposed  connection between different CG systems is prescribed by  (i) the CG representation (mapping) and (ii) the functional  form and parameters used to represent the CG energetics,  which approximate potentials of mean force (PMFs). As a  result, the design of CG methods that facilitate a variety  of physically relevant representations, approximations, and  force fields is critical to moving the frontier of  systematic CG forward. Crucially, the proposed connection  between the system used for parametrization and the system  of interest is orthogonal to the optimization used to  approximate the potential of mean force present in all  systematic CG methods. The empirical efficacy of machine  learning techniques on a variety of tasks provides strong  motivation to consider these approaches for approximating  the PMF and analyzing these approximations.},
      url = {http://knowledge.uchicago.edu/record/5433},
}