Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
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DOI：
10.1021/acs.jpcb.2c08731
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发表时间：
2023-05-07
影响因子：
3.3
通讯作者：
Noid，W. G.
中科院分区：
文献类型：
--
作者：
Noid，W. G.
通过对原子细节进行平均，粗粒度（CG）模型为研究软材料提供了深刻的计算和概念优势。特别是，自下而上的方法开发CG模型的基础上获得的信息从原子详细的模型。至少在原则上，自下而上模型可以再现在CG模型的分辨率下可观察到的原子详细模型的所有属性。从历史上看，自下而上的方法已经准确地模拟了液体，聚合物和其他无定形软材料的结构，但对更复杂的生物分子系统提供了较低的结构保真度。此外，它们还受到不可预测的可转移性和热力学性质描述不佳的困扰。幸运的是，最近的研究报告在解决这些先前的限制方面取得了巨大进展。本观点回顾了这一显着的进展，同时集中在其基础上的粗粒化的基本理论。特别是，我们描述了最近的见解和进展，治疗CG映射，多体相互作用建模，用于解决有效势的状态点依赖性，甚至用于再现原子的可观测量，超出了CG模型的分辨率。我们还概述了该领域的突出挑战和有希望的方向。我们预计，严格的理论和现代计算工具的综合将导致实际的自下而上的方法，不仅是准确的和可转移的，而且还提供了复杂系统的预测洞察力。
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.