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.

该项目的重点是开发和优化MB-Fit/MBX软件基础设施，这是一种旨在推进分子模拟领域的工具。通过提供分子相互作用的机器学习表示，该软件为世界各地的研究人员提供了在分子水平上以前所未有的准确度建模和预测复杂系统行为的可能性。作为一个开源工具，MB-Fit/MBX不仅通过鼓励全球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民主化。该项目致力于通过组织研讨会和培训计划促进教育和多样性，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计划以及与历史上的黑人学院和大学（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。这一项目的重要性突出体现在它有可能通过提供一个具有广泛应用的工具来促进科学知识和促进国家进步。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，重点介绍了MB-Fit/MBX软件基础设施所实现的所有科学成就和新发现。该平台不仅传播了项目的成果，还有助于提高社区贡献者的知名度，从而培养认可和合作的文化。该项目的主要目标包括开发MBX-Fit/MBX软件基础设施，用于数据驱动的多体分子模拟，加速计算密集型术语到GPU加速器，以及MBX-Fit/MBX软件与LAMMPS、i-PI和RASPA的集成，这些软件是广泛用于分子模拟的开源软件。该项目还旨在增强MBX中迭代静电求解器的可扩展性，并开发迷你应用程序，以有效评估CPU和GPU加速器上的PIP。该项目采用了各种方法和途径，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的子集将负责计算3D FFT，为性能优化提供额外的机会。微型应用程序的开发将是软件设计战略的关键要素，可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由分析工具指导的性能调优和优化，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和有效的分子模拟，培养用户和开发人员社区，并为该领域的培训和教育提供平台。该项目的进展和成就将通过博客进行传播，该博客重点介绍MB-Fit/MBX软件基础设施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。该项目的更广泛的影响包括组织研讨会和培训计划，本科生暑期研究计划，以及与历史上的黑人学院和大学（HBCU）的合作，所有这些都旨在在代表性不足和弱势社区推广STEM学科。高级网络基础设施办公室颁发的该奖项由数学和物理科学理事会化学部门联合支持。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。