基于结构的粗粒化中广泛的化学转移性
Broad chemical transferability in structure-based coarse-graining
摘要：
与自顶向下的粗粒度（CG）模型相比，自底向上的方法能够提供更高的结构保真度。这种保真度来自与更高分辨率参考的紧密联系，使CG模型具有化学特异性。不幸的是，化学特异性可能与化合物筛选策略不一致，这需要可转移的参数化。在这里，我们提出了一种方法来调和自下而上，结构保持CG模型与化学转移。我们考虑了3，441个C $_7$ O $_2$小分子异构体的自底向上CG参数化。我们的方法结合了原子表示，无监督学习和大规模扩展集成力匹配参数化。我们首先确定了一个子集的19个代表性的分子，最大限度地编码的所有气相构象的局部环境。从均质散装液体和各种二元混合物中获得19个代表性分子之间的参考相互作用。一个扩展的合奏参数化所有703个状态点，导致CG模型，这是基于结构和化学转移。值得注意的是，由此产生的力场在结构上平均比单一状态点等效物更精确。对扩展系综进行平均可以作为平均力正则化器，消除对单个状态点过于特定的力和结构相关性。我们的方法旨在通过一组CG珠类型实现可转移性，这些CG珠类型可用于轻松构建新分子，同时保留基于结构的参数化的优点。
英文摘要：
Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parametrizations. Here we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parametrization of 3,441 C$_7$O$_2$ small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parametrization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parametrization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules, while retaining the benefits of a structure-based parametrization.
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