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By averaging over atomic details, coarse-grained (CG) models provide profound computational and conceptual advantages for studying soft materials. In particular, bottom-up approaches develop CG models based upon information obtained from atomically detailed models. At least in principle, a bottom-up model can reproduce all the properties of an atomically detailed model that are observable at the resolution of the CG model. Historically, bottom-up approaches have accurately modeled the structure of liquids, polymers, and other amorphous soft materials, but have provided lower structural fidelity for more complex biomolecular systems. Moreover, they have also been plagued by unpredictable transferability and a poor description of thermodynamic properties. Fortunately, recent studies have reported dramatic advances in addressing these prior limitations. This Perspective reviews this remarkable progress, while focusing on its foundation in the basic theory of coarse-graining. In particular, we describe recent insights and advances for treating the CG mapping, for modeling many-body interactions, for addressing the state-point dependence of effective potentials, and even for reproducing atomic observables that are beyond the resolution of the CG model. We also outline outstanding challenges and promising directions in the field. We anticipate that the synthesis of rigorous theory and modern computational tools will result in practical bottom-up methods that not only are accurate and transferable but also provide predictive insight for complex systems.
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Rigorous progress in coarse-graining
Low resolution coarse-grained (CG) models provide remarkable com- putational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to ma- chine learning methods. We then discuss recent approaches for si- multaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density- and temperature-dependence of these potentials. We also briefly dis- cuss exciting progress in modeling high resolution observables with low- resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understand- ing the limitations of prior CG models, but also for developing robust computational methods that resolve these limitations in practice.
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- PAR ID:
- 10499895
- Publisher / Repository:
- Annual Review of Physical Chemistry
- Date Published:
- Journal Name:
- Annual review of physical chemistry
- Volume:
- 75
- ISSN:
- 1545-1593
- Page Range / eLocation ID:
- 21-45
- Subject(s) / Keyword(s):
- coarse-graining effective potentials soft materials transferability representability statistical mechanics fluctuations machine learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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