Frameworks: Data-Driven Software Infrastructure for Next-Generation Molecular Simulations

框架：下一代分子模拟的数据驱动软件基础设施

• 批准号: 2311260
• 负责人: Francesco Paesani
• 金额: $ 292.81万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311260&HistoricalAwards=false
• 关键词: Frameworks; Data; Driven; Software; Infrastructure

This project focuses on the development and optimization of the MB-Fit/MBX software infrastructure, a tool designed to advance the field of molecular simulations. By providing a machine-learned representation of molecular interactions, the software provides researchers worldwide with the possibility to model and predict the behavior of complex systems at the molecular level with unprecedented accuracy. As an open-source tool, MB-Fit/MBX not only promotes the progress of science by encouraging contributions from researchers worldwide but also democratizes access to cutting-edge computational tools. The project is committed to fostering education and diversity through the organization of workshops and training programs, thereby creating a vibrant community of users and developers. These initiatives include undergraduate summer research programs and collaborations with Historically Black Colleges and Universities (HBCUs), which aim to attract students to computational molecular sciences and promote STEM disciplines in underrepresented and underprivileged communities. The significance of this project is underscored by its potential to advance scientific knowledge and contribute to national progress by providing a tool with wide-ranging applications. From drug design to materials science, the MB-Fit/MBX software infrastructure is poised to catalyze breakthroughs across various scientific domains. The project also includes a blog that highlights all scientific accomplishments and new discoveries enabled by the MB-Fit/MBX software infrastructure. This platform not only disseminates the project's achievements but also helps to raise the visibility of contributors in the community, thereby fostering a culture of recognition and collaboration.The primary goals of this project include the development of the MBX-Fit/MBX software infrastructure for data-driven many-body molecular simulations, acceleration of computationally intensive terms to GPU accelerators, and integration of the MBX-Fit/MBX software with LAMMPS, i-PI, and RASPA, which are widely used open-source software for molecular simulations. The project also aims to enhance the scalability of the iterative electrostatic solver in MBX and develop mini-apps for efficient evaluation of PIPs on CPUs and GPU accelerators. The project employs a variety of methods and approaches, including machine learning, high-performance computing, and open-source software development. Specifically, the project will explore multi-partition algorithms where a subset of the CPUs will be tasked with computing the 3D FFTs, providing additional opportunities for performance optimization. The development of mini-apps will be a key element of the software design strategy, enabling rapid algorithm development and performance measurement on target architectures. The project will also focus on performance tuning and optimization guided by profiling tools, with a particular focus on achieving better performance with code refactoring. As a result, this project will enable more accurate and efficient molecular simulations, foster a community of users and developers, and provide a platform for training and education in the field. The progress and achievements of the project will be disseminated through a blog that highlights scientific accomplishments and new discoveries enabled by the MB-Fit/MBX software infrastructure. The project also includes a manual on the MB-Fit/MBX website, providing comprehensive guidance for users and developers. The project will benefit from periodic interactions with LAMMPS, i-PI, and RASPA developers to ensure the best performance of the MBX/LAMMPS, MBX/i-PI, and MBX/RASPA interfaces. The project's broader impacts include the organization of workshops and training programs, undergraduate summer research programs, and collaborations with Historically Black Colleges and Universities (HBCUs), all aimed at promoting STEM disciplines in underrepresented and underprivileged communities.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry within the Directorate for Mathematical and Physical Sciences.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.

该项目专注于 MB-Fit/MBX 软件基础设施的开发和优化，这是一种旨在推进分子模拟领域发展的工具。通过提供分子相互作用的机器学习表示，该软件为全世界的研究人员提供了在分子水平上以前所未有的精度建模和预测复杂系统行为的可能性。作为一种开源工具，MB-Fit/MBX 不仅通过鼓励全世界研究人员的贡献来促进科学进步，而且还使尖端计算工具的使用变得民主化。该项目致力于通过组织研讨会和培训计划来促进教育和多样性，从而创建一个充满活力的用户和开发人员社区。这些举措包括本科生暑期研究项目以及与传统黑人学院和大学 (HBCU) 的合作，旨在吸引学生学习计算分子科学，并在代表性不足和贫困社区推广 STEM 学科。该项目的重要性在于其通过提供具有广泛应用的工具来推进科学知识并为国家进步做出贡献的潜力。从药物设计到材料科学，MB-Fit/MBX 软件基础设施有望促进各个科学领域的突破。该项目还包括一个博客，重点介绍 MB-Fit/MBX 软件基础设施实现的所有科学成就和新发现。该平台不仅传播项目成果，还有助于提高社区中贡献者的知名度，从而培育认可和协作的文化。该项目的主要目标包括开发用于数据的 MBX-Fit/MBX 软件基础设施-驱动的多体分子模拟、GPU 加速器的计算密集型项加速，以及 MBX-Fit/MBX 软件与 LAMMPS、i-PI 和 RASPA 的集成，这些软件广泛用于分子分析领域模拟。该项目还旨在增强 MBX 中迭代静电求解器的可扩展性，并开发用于在 CPU 和 GPU 加速器上高效评估 PIP 的迷你应用程序。该项目采用了多种方法和途径，包括机器学习、高性能计算和开源软件开发。具体来说，该项目将探索多分区算法，其中 CPU 子集将负责计算 3D FFT，从而为性能优化提供额外的机会。迷你应用程序的开发将成为软件设计策略的关键要素，从而实现目标架构上的快速算法开发和性能测量。该项目还将重点关注分析工具指导下的性能调整和优化，特别关注通过代码重构实现更好的性能。因此，该项目将实现更准确、更高效的分子模拟，培育用户和开发人员社区，并为该领域的培训和教育提供平台。该项目的进展和成果将通过博客传播，重点介绍 MB-Fit/MBX 软件基础设施所实现的科学成就和新发现。该项目还包括 MB-Fit/MBX 网站上的手册，为用户和开发人员提供全面的指导。该项目将受益于与 LAMMPS、i-PI 和 RASPA 开发人员的定期互动，以确保 MBX/LAMMPS、MBX/i-PI 和 MBX/RASPA 接口的最佳性能。该项目的更广泛影响包括组织研讨会和培训计划、本科生暑期研究计划以及与传统黑人学院和大学 (HBCU) 的合作，所有这些都旨在在代表性不足和贫困社区推广 STEM 学科。该奖项由高级网络基础设施办公室颁发由数学和物理科学理事会化学部共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和评估进行评估，认为值得支持。更广泛的影响审查标准。