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.

分子模拟提供了一个强大的补充，传统的实验技术，提供高分辨率的信息和不寻常的水平控制的实验条件。虽然原子分辨率的分子模拟已经得到了很好的建立和广泛的应用，但也可以从分子表示中去除无关的细节，以创建高效的“粗粒度”（CG）模型。CG方法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模型的计算效率使科学家不仅可以研究更大的系统，而且可以研究需要更长时间尺度的现象。这些CG方法是特别感兴趣的研究系统中出现的各种过程的关键方面从大量的分子之间的相互作用在相对较长的距离。因此，CG模型可以提供对此类系统的分子基础的重要见解，例如，新材料然而，CG模型可能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，该项目将在广泛使用的LAMMPS分子动力学模拟代码中实现关键算法的进步和相关的CG功能。此外，该项目还将建立一个公众可访问的CG模型参数和输入文件库，以加速CG模型范例在整个科学界的传播。该项目将把超大规模和动态CG模型的关键功能集成到LAMMPS分子动力学软件包中。这些功能不仅包括稀疏存储器优化（例如， 模板分子拓扑描述和空间数据结构的链接细胞算法），而且用户定义的过渡信息的传播“超粗粒”（UCG）模型;后者的参数化可以实现通过使用集成的多尺度粗粒力匹配代码（MSCGFM）。此外，直接纳入CG模型的实验数据将有助于实现的“实验指导元自相关”（EDM）和“实验指导模拟”（EDS）算法。总之，这些增强功能将为广泛的用户社区提供尖端的CG模型生成和仿真技术。为了补充LAMMPS代码的扩展功能，将提供一个用户驱动的CG模型数据和元数据储存库，以协助向科学界有效传播模型参数和模拟/验证数据。