标题： 细化粗粒度分子拓扑：一种贝叶斯优化方法 显示英文标题

标题： Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach

摘要： 分子动力学（MD）模拟对于准确预测大分子系统在各种压力和温度系综下的物理和化学性质至关重要。 然而，全原子（AA）MD模拟所涉及的高计算成本促使了粗粒度分子动力学（CGMD）的发展，它通过将AA结构压缩为代表性的粗粒度珠子，提供了更低维度的简化，从而降低了计算成本，但牺牲了一定的预测准确性。 现有的CGMD方法，如基于实验数据校准的CG-Martini，旨在生成一个拓扑嵌入，能够在广泛的结构范围内充分泛化。 不利的是，在试图指定适用于多种分子类别的参数化时，这种方法无法专门针对领域特定的应用，而这些应用中足够的准确性和计算速度至关重要。 本研究提出了一种新颖的方法，通过使用贝叶斯优化方法优化通用Martini3拓扑中的键合相互作用参数，从而改进CGMD模拟的结果，以适应领域特定的应用。 我们开发并验证了一种适用于任意聚合度的粗粒度势能，这在该领域是一项重大进展。 我们的优化粗粒度势能基于Martini3框架，旨在实现与全原子MD（AAMD）相当的精度，同时保持CGMD的计算效率。 这种方法弥合了多尺度分子模拟中效率和准确性之间的差距，可能使在各种科学和技术领域内的分子发现更加迅速且更具成本效益。 显示英文摘要

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.