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

CAREER: Develop a Hybrid Adaptive Particle-Field Simulation Method for Solutions of Macromolecules and a New Computational Chemistry Course for Lower-Division Undergraduates
职业:开发用于大分子解决方案的混合自适应粒子场模拟方法以及低年级本科生的新计算化学课程
批准号:
2337602
负责人:
Bilin Zhuang
金额:
$61.89万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-01-01 至 2028-12-31

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中文摘要
翻译
在化学学部化学理论、模型和计算方法(CTMC)项目的支持下,哈维马德学院的庄碧林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不可缺少的工具,但对于溶液中的大分子系统,如聚合物和蛋白质组合,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努力开发一种模拟方法,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。该方法有望为模拟从简单离子到蛋白质的溶液中的大分子提供一种方便和加速的工具。要实现这些目标,还需要对本科生和高中生进行计算研究方面的培训,并为本科一年级学生开设新的计算化学课程。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)通过合作研究型项目吸引学生积极发现,3)创建使用计算促进学生学习的入门级教学资源。这被看作是填补了这类课程在化学教育界仍然稀缺的需求。本研究开发的新型混合自适应粒子场模拟方法有望为所有子学科的化学家提供可用的计算化学工具工具箱做出有意义的贡献。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价性,允许人们用更多的分子细节(类粒子)处理空间中选择区域的分子,而在其他区域用更少的分子细节(类场)处理分子。类粒子区和类场区可以适应大分子在飞行中的构象,区域之间没有突然的边界。PI已经为所提出的方法推导了配分函数,并将通过实施和验证仿真方案并将该方法应用于研究聚电解质刷和肽水凝胶来实现研究目标。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
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.
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