SI2-SSE: Highly Efficient and Scalable Software for Coarse-Grained Molecular Dynamics

SI2-SSE：高效且可扩展的粗粒度分子动力学软件

• 批准号: 1740211
• 负责人: Gregory Voth
• 金额: $ 50万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740211&HistoricalAwards=false
• 关键词: SI2; SSE; Highly; Efficient; Scalable

Molecular simulation provides a powerful complement to conventional experimental techniques, offering both high-resolution information and unusual levels of control over the experimental conditions. While atomic-resolution molecular simulations are well established and widely used, it is also possible to remove extraneous detail from molecular representations to create highly efficient "coarse-grained" (CG) models. CG approaches can expand the potential applications of molecular simulations far beyond atomic-resolution models: the computational efficiency of CG models allows the scientist to investigate not only significantly larger systems, but also phenomena that require significantly longer time scales. These CG approaches are of particular interest in the study of systems where key aspects of various processes emerge from interactions between large numbers of molecules over relatively long distances. CG models can therefore provide crucial insight into the molecular basis of such systems, e.g., new materials. However, CG models can require significant scientific understanding to create and use effectively. To bring cutting-edge CG methodologies into a wider degree of use, this project will implement key algorithmic advances and associated CG functionalities into the widely-used LAMMPS molecular dynamics simulation code. Furthermore, the project will implement a publically-accessible repository for CG model parameters and input files to accelerate the dissemination of exemplar CG models throughout the scientific community.The project will integrate key functionalities for very large-scale and dynamic CG models into the LAMMPS molecular dynamics package. These functionalities include not only sparse memory optimizations (e.g., template molecular topology descriptions and spatial data structures for link cell algorithms) but also user-defined transition information for the propagation of "ultra-coarse-grained" (UCG) models; parameterization of the latter can be achieved by using the integrated multi-scale coarse-grained force matching code (MSCGFM). Furthermore, direct incorporation of experimental data into CG models will be assisted by implementations of the "experiment directed metadynamics" (EDM) and "experiment directed simulation" (EDS) algorithms. Taken together, these enhancements will provide cutting-edge CG model generation and simulation techniques to a wide user community. To complement the extended functionality of the LAMMPS code, a user-driven data and metadata repository for CG models will be provided to assist with efficient dissemination of model parameters and simulation/validation data to the scientific community.

分子模拟为传统实验技术提供了强有力的补充，提供了高分辨率信息和对实验条件的异常控制水平。虽然原子分辨率分子模拟已经成熟并广泛使用，但也可以从分子中去除无关的细节。创建高效“粗粒度”(CG) 模型的表示可以将分子模拟的潜在应用扩展到原子分辨率模型之外：CG 模型的计算效率不仅使科学家能够研究更大的模型。这些 CG 方法对于研究系统特别感兴趣，其中各种过程的关键方面是由相对较长距离的大量分子之间的相互作用产生的，因此可以提供重要的见解。然而，CG 模型需要大量的科学理解才能有效地创建和使用，为了使尖端的 CG 方法得到更广泛的应用，该项目将实施关键的算法进步和技术。联系此外，该项目还将实现一个可公开访问的 CG 模型参数和输入文件存储库，以加速示例 CG 模型在科学界的传播。该项目将在整个过程中集成关键功能。用于非常大规模和动态 CG 模型到 LAMMPS 分子动力学包中这些功能不仅包括稀疏内存优化（例如，模板分子拓扑描述和链接的空间数据结构）。单元算法）以及用于传播“超粗粒度”（UCG）模型的用户定义的转换信息；后者的参数化可以通过使用集成的多尺度粗粒度力匹配代码（MSCGFM）来实现。此外，“实验定向元动力学”(EDM) 和“实验定向模拟”(EDS) 算法的实施将有助于将实验数据直接纳入 CG 模型。这些增强功能将为广泛的用户社区提供尖端的 CG 模型生成和模拟技术，以补充 LAMMPS 代码的扩展功能，将为 CG 模型提供用户驱动的数据和元数据存储库，以协助模型的有效传播。向科学界提供参数和模拟/验证数据。