细化粗粒化分子拓扑：一种贝叶斯优化方法
Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach
AI总结：
本文提出用贝叶斯优化细化Martini3粗粒化拓扑中的键合参数，开发出适用于任意聚合度且精度接近全原子MD的CG势能，兼顾计算效率与精度。
摘要：
分子动力学（MD）模拟对于在各种压力和温度系综下准确预测大分子体系的物理和化学性质至关重要。然而，全原子（AA）MD模拟的高计算成本推动了粗粒化分子动力学（CGMD）的发展，该方法将AA结构低维压缩为具有代表性的CG珠子，以牺牲预测精度为代价降低计算开销。现有的CGMD方法（如针对实验数据校准的CG-Martini）旨在生成一种能在多种结构间充分泛化的拓扑嵌入。不利的是，在试图指定跨分子类别的参数化时，它无法专门化到对足够精度和计算速度至关重要的领域特定应用。本工作提出了一种新方法，通过使用贝叶斯优化方法，针对领域特定应用细化通用Martini3拓扑——具体是给定粗粒化映射中的键合相互作用参数——来优化CGMD模拟的派生结果。我们开发并验证了一种适用于任意聚合度的CG势能，代表了该领域的重大进展。我们基于Martini3框架优化的CG势能旨在达到与AAMD相当的精度，同时保持CGMD的计算效率。该方法弥合了多尺度分子模拟中效率与精度之间的差距，有望在多个科学和技术领域实现更快速、更具成本效益的分子发现。
英文摘要：
Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature ensembles. However, the high computational costs associated with All-Atom (AA) MD simulations have led to the development of Coarse-Grained Molecular Dynamics (CGMD), providing a lower-dimensional compression of the AA structure into representative CG beads, offering reduced computational expense at the cost of predictive accuracy. Existing CGMD methods, such as CG-Martini (calibrated against experimental data), aim to generate an embedding of a topology that sufficiently generalizes across a range of structures. Detrimentally, in attempting to specify parameterization with applicability across molecular classes, it is unable to specialize to domain-specific applications, where sufficient accuracy and computational speed are critical. This work presents a novel approach to optimize derived results from CGMD simulations by refining the general-purpose Martini3 topologies specifically the bonded interaction parameters within a given coarse-grained mapping - for domain-specific applications using Bayesian Optimization methodologies. We have developed and validated a CG potential applicable to any degree of polymerization, representing a significant advancement in the field. Our optimized CG potential, based on the Martini3 framework, aims to achieve accuracy comparable to AAMD while maintaining the computational efficiency of CGMD. This approach bridges the gap between efficiency and accuracy in multiscale molecular simulations, potentially enabling more rapid and cost-effective molecular discovery across various scientific and technological domains.
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