CP2K For Emerging Architectures And Machine Learning

适用于新兴架构和机器学习的 CP2K

• 批准号: EP/W030489/1
• 负责人: Matthew Watkins
• 金额: $ 67.01万
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW030489%2F1
• 关键词: CP2K; Emerging; Architectures; Machine; Learning

The CP2K software (www.cp2k.org) is a highly efficient and parallelizable open-source atomistic simulation tool able to calculate the energies and forces (as well as other properties) of large collections of atoms at a variety of levels of theory. This makes it a prime candidate for usage in novel machine learning and other data driven areas of research as well as more traditional materials science work. Indeed, CP2K was one of the most intensively used codes on ARCHER and has an extensive and growing user base on ARCHER2. CP2K was one of the acceptance test codes evaluated on ARCHER2, where it underperformed in comparison to the average of the suite of test codes, highlighting the need for this code to be refactored and optimised for the ARCHER2 hardware and emerging systems like Bede. In addition to the clear need to tune CP2K to the ARCHER2 architecture, there are two other main drivers for our bid: 1) to support the UK user base of ~200 researchers through user meetings and workshops 2) develop CP2K so that it can become the favoured density functional theory (DFT) engine for the rapidly expanding community applying machine learning (ML) methods to materials and structure prediction. Previously, we have obtained funding to support the CP2K community and develop the CP2K code. The "CP2K-UK" grant ran from 2013 to 2018 and helped build a large, connected and productive community of CP2K users and developers in the UK. Annual meetings typically attracted around 100 attendees demonstrating a clear demand from CP2K users. Usage of CP2K on national supercomputers grew significantly during this period. Currently, we are experiencing a dramatic shift in the way that the materials modelling community tackles scientific problems as ML and artificial intelligence transform more areas of research. For machine learning there are two application scenarios: (1) machine learning interaction potentials, and (2) machine learning molecular/materials properties. For (1), CP2K can provide energies and forces, and for (2) CP2K can provide a range of properties that can be learned. We will address these scenarios by providing software for: - Rapid and efficient sampling of high-quality ab initio data - Easy and reproducible environments for developing and utilizing ML potentials - Clear documentation of workflows and scientific method - Better integration with materials databases allowing data-mining of results. CP2K is particularly well placed to address these challenges through its intrinsic efficiency in generating data. This also means that less energy is used for during the training process of building ML methods helping the UK's net zero targets. Because CP2K is open source with a sustained and growing development base for over a decade and a clear code development ethos, it is readily amenable for integration with other software and libraries. We will support and expand this extremely successful community and develop a suite of tools for CP2K to enable highly efficient, reproducible, and flexible workflows on the new and next generation of UK hardware and the emerging generation of materials modelers that rely upon ML methods. We will provide community led improvements to CP2K, develop a flexible and robust ML potential work environment encompassing CP2K and partner ML codes. The community will be grown by a series of hands-on workshops that encompass both local and international experts and use both traditional presentation and online learning materials. We will also extend our activities to industrial partners including Johnson Matthey.

CP2K 软件 (www.cp2k.org) 是一种高效且可并行的开源原子模拟工具，能够在各种理论水平上计算大量原子的能量和力（以及其他属性）。这使其成为新型机器学习和其他数据驱动研究领域以及更传统的材料科学工作的主要候选者。事实上，CP2K 是 ARCHER 上使用最频繁的代码之一，并且在 ARCHER2 上拥有广泛且不断增长的用户群。 CP2K 是在 ARCHER2 上评估的验收测试代码之一，与测试代码套件的平均水平相比，它的表现不佳，这突出表明需要针对 ARCHER2 硬件和 Bede 等新兴系统重构和优化该代码。除了将 CP2K 调整到 ARCHER2 架构的明确需求之外，我们的投标还有另外两个主要驱动力：1) 通过用户会议和研讨会支持约 200 名研究人员的英国用户群 2) 开发 CP2K，使其成为快速扩展的将机器学习 (ML) 方法应用于材料和结构预测的社区所青睐的密度泛函理论 (DFT) 引擎。此前，我们已获得资金支持 CP2K 社区并开发 CP2K 代码。 “CP2K-UK”赠款从 2013 年持续到 2018 年，帮助在英国建立了一个由 CP2K 用户和开发者组成的大型、互联且高效的社区。年会通常会吸引大约 100 名与会者，这表明 CP2K 用户有明确的需求。在此期间，CP2K 在国家超级计算机上的使用量显着增长。目前，随着机器学习和人工智能改变更多的研究领域，材料建模社区解决科学问题的方式正在发生巨大转变。机器学习有两个应用场景：（1）机器学习交互势，（2）机器学习分子/材料属性。对于（1），CP2K可以提供能量和力，对于（2）CP2K可以提供一系列可以学习的属性。我们将通过提供以下软件来解决这些场景： - 快速高效地采样高质量的从头开始数据 - 用于开发和利用机器学习潜力的简单且可重复的环境 - 清晰的工作流程和科学方法文档 - 与材料数据库更好地集成，允许对结果进行数据挖掘。 CP2K 特别适合通过其生成数据的内在效率来应对这些挑战。这也意味着在构建机器学习方法的培训过程中使用更少的能源来帮助英国实现净零目标。由于 CP2K 是开源的，拥有十多年来持续不断增长的开发基础和明确的代码开发精神，因此它很容易与其他软件和库集成。我们将支持和扩展这个极其成功的社区，并为 CP2K 开发一套工具，以在新一代英国硬件和依赖机器学习方法的新兴一代材料建模器上实现高效、可重复且灵活的工作流程。我们将为 CP2K 提供社区主导的改进，开发一个灵活且强大的 ML 潜在工作环境，其中包含 CP2K 和合作伙伴 ML 代码。该社区将通过一系列由本地和国际专家组成的实践研讨会来发展，并使用传统的演示和在线学习材料。我们还将把我们的活动扩展到包括庄信万丰在内的工业合作伙伴。