CAREER: Develop a Hybrid Adaptive Particle-Field Simulation Method for Solutions of Macromolecules and a New Computational Chemistry Course for Lower-Division Undergraduates

在化学系化学理论，模型和计算方法（CTMC）计划的支持下，Harvey Mudd College的Bilin Zhuang博士正在开发一种混合模拟方法来模拟溶液中的大分子。 全原子模拟已成为研究分子间相互作用的不可或缺的工具，但对于溶液中的大分子系统，如聚合物和蛋白质组装体，它们的成本非常高。为了实现这些模拟，庄博士和她的研究小组将致力于开发一种模拟方法，该方法可以大大减少用分子细节描述的溶剂分子的数量，同时捕获正确的溶质-溶剂相互作用和动力学。该方法有望为模拟溶液中从简单离子到蛋白质的大分子提供一种方便快捷的工具。 实现这些目标还将涉及培训本科生和高中生的计算研究和开发一个新的计算化学课程的一年级本科生。新课程将1）介绍计算在化学中的广泛应用，从分子建模到数据科学，2）通过协作研究型项目让学生积极发现，3）创建使用计算促进学生学习的入门级教学资源。 这被视为填补了需要，因为这样的课程仍然是稀缺的化学教学界。 在这项研究中开发的新的混合自适应粒子场模拟方法，预计将在所有子学科的化学家可用的计算化学工具的工具箱作出有意义的贡献。 该模拟方法将利用统计力学中基于粒子和基于场的表示之间的等效性，允许在选定的空间区域中处理具有更多分子细节（类粒子）的分子，并在其他区域中处理具有更少分子细节（类场）的分子。 颗粒状和场状区域可以适应大分子的动态构象，区域之间没有突然的边界。PI已经推导出了所提出的方法的分区函数，并将通过实施和验证模拟方案，并将该方法应用于研究生物刷和肽水凝胶来实现研究目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Dr. Bilin Zhuang of Harvey Mudd College is developing a hybrid simulation method to simulate large molecules in solution. All-atom simulations have become an indispensable tool for studying interactions between molecules, but they are very costly for systems of large molecules in solution, such as polymers and protein assemblies. To enable these simulations, Dr. Zhuang and her research group will work to develop a simulation method that can greatly reduce the number of solvent molecules described with molecular detail while capturing the correct solute-solvent interactions and dynamics. This method is expected to provide a convenient and accelerated tool for simulating large molecules in solution ranging from simple ions to proteins. Achieving these goals will also involve training undergraduates and high school students in computational research and developing a new computational chemistry course for first-year undergraduate students. The new course will 1) introduce the broad usage of computation in chemistry from molecular modeling to data sciences, 2) engage students in active discovery through collaborative research-oriented projects, and 3) create introductory-level teaching resources that use computation to facilitate student learning. This is seen as filling a need as such courses are still scarce in the chemistry teaching community. The new hybrid adaptive particle-field simulation method being developed in this research is expected to make a meaningful contribution to the toolbox of available computational chemistry tools for chemists in all subdisciplines. The simulation method will exploit the equivalence between particle-based and field-based representations in statistical mechanics, allowing one to treat molecules in selected regions of space with more molecular detail (particle-like) and in other regions with fewer molecular details (field-like). The particle-like and field-like regions may adapt to the conformation of the large molecule on the fly, with no abrupt boundary between the regions. The PI has derived the partition function for the proposed method and will achieve the research objectives by implementing and validating the simulation scheme and applying the method to investigate polyelectrolyte brushes and peptide hydrogels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.