CP2K For Emerging Architectures And Machine Learning

CP 2K软件（www.cp2k.org）是一个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）。这使得它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，CP 2K是ARCHER上使用最广泛的代码之一，并且在ARCHER 2上拥有广泛且不断增长的用户群。CP 2K是在ARCHER 2上评估的验收测试代码之一，与测试代码套件的平均值相比，它的表现不佳，突出了该代码需要针对ARCHER 2硬件和新兴系统（如Bede）进行重构和优化。除了将CP 2K调整到ARCHER 2架构的明确需求外，我们的竞标还有另外两个主要驱动因素：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CP 2K，以便它可以成为快速扩展的社区将机器学习（ML）方法应用于材料和结构预测的首选密度泛函理论（DFT）引擎。此前，我们已经获得了支持CP 2K社区和开发CP 2K代码的资金。“CP 2K-UK”赠款从2013年持续到2018年，帮助在英国建立了一个大型的、相互联系的、富有成效的CP 2K用户和开发人员社区。年度会议通常吸引约100名与会者，表明CP 2K用户的明确需求。在此期间，CP 2K在国家超级计算机上的使用量大幅增长。目前，随着ML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的变化。对于机器学习，有两种应用场景：（1）机器学习相互作用潜力，以及（2）机器学习分子/材料特性。对于（1），CP 2K可以提供能量和力，对于（2），CP 2K可以提供一系列可以学习的属性。我们将通过提供软件来解决这些问题：-快速有效地采样高质量的从头算数据-开发和利用ML潜力的简单和可重复的环境-清晰的工作流程和科学方法文档-更好地与材料数据库集成，允许结果的数据挖掘。CP 2K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ML方法的训练过程中使用更少的能量，帮助英国实现净零目标。由于CP 2K是开源的，具有十多年持续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非常成功的社区，并为CP 2K开发一套工具，以在新一代和下一代英国硬件以及依赖ML方法的新一代材料建模器上实现高效，可重复和灵活的工作流程。我们将提供社区主导的对CP 2K的改进，开发一个灵活而强大的ML潜在工作环境，包括CP 2K和合作伙伴ML代码。社区将通过一系列实践研讨会来发展，这些研讨会包括当地和国际专家，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约翰逊万丰在内的工业合作伙伴。

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.