Goldilocks convergence tools and best practices for numerical approximations in Density Functional Theory calculations

密度泛函理论计算中数值近似的金发姑娘收敛工具和最佳实践

• 批准号: EP/Z530657/1
• 负责人: Barbara Montanari
• 金额: $ 48.19万
• 依托单位: Science and Technology Facilities Council
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ530657%2F1
• 关键词: Goldilocks; convergence; tools; best; practices

Within the field of materials and molecular science, modelling and simulation based on Density Functional Theory (DFT) is key in the R&D of functional materials for environmental sustainability, such as green computing, environment remediation, and energy production, conversion and storage. DFT-based research currently consumes a considerable amount of resources on supercomputers globally. In the UK, DFT calculations use over 45% of ARCHER2, the Tier1 UK National Supercomputing service. DFT also features heavily in the usage of Tier2 systems and lower-tier institutional computers. As ever more powerful computers become available, the environmental impact of DFT-based research is increasing rapidly. It is paramount to improve the efficiency of this research and develop means of assuring that energy-intensive compute resources are distributed and used responsibly. The proposed work will provide practical tools and evidence-based best practices towards these aims for researchers and the compute-resources distribution chain.DFT calculations contain numerical approximations that need to be converged according to the accuracy required for each study. Without more support for inexperienced users, the risk of is of over-convergence, leading to unnecessarily more costly calculations, or under-convergence, leading to entirely useless calculations, which are a waste of compute resource and electricity. A conservative estimate of the proportion of under- or over-converged DFT calculations is in the 10% range. Given the large proportion of compute resource invested in this research, even a relatively small increase in efficiency will result in a large reduction of wasted compute resource, and significant improvements in the environmental sustainability of research infrastructure.This project will result in a tool and evidence-based best practices to provide automatic, expert guiding in the 'Goldilocks' choice of these convergence parameters. This will be achieved by training machine learning (ML) models to predict the convergence parameters for DFT numerical approximations for the required accuracy in common types of scientific investigations. Given that numerical approximations requiring convergence are present in all codes, this tool will be applicable across all DFT codes in common use in the UK. The primary contribution of this project will be to increase considerably the efficiency and assurance levels of responsible use of UKRI and EPSRC hardware and software infrastructure, now and in the future.Comparison of the compute resources usage for typical jobs run before and after the adoption of this tool, will enable baseline quantification and extrapolation of the efficiency gained. Outcomes of this analysis will be disseminated globally, leading to best practices across international compute Facilities, so as to extend world-wide the gains in environmental sustainability of compute infrastructure.This project will be a significant step towards ML-based automatic generation of inputs for DFT calculations, as well as an automatic a priori calculator of compute resources and carbon footprint. This automation will contribute to democratisation in the use of this research method in parts of the world where digital research infrastructure may be more accessible than experimental facilities.

在材料和分子科学领域，基于密度泛函理论（DFT）的建模和模拟是环境可持续性功能材料研发的关键，如绿色计算、环境修复以及能源生产、转换和储存。基于DFT的研究目前在全球超级计算机上消耗了大量资源。在英国，DFT计算使用超过45%的ARCHER 2，英国国家超级计算服务的第一层。DFT还大量使用Tier2系统和较低层的机构计算机。随着越来越强大的计算机变得可用，基于DFT的研究对环境的影响正在迅速增加。提高这项研究的效率并开发确保能源密集型计算资源的分配和使用的方法至关重要。拟议的工作将为研究人员和计算资源分配链提供实现这些目标的实用工具和基于证据的最佳实践。DFT计算包含需要根据每个研究所需的精度进行收敛的数值近似。如果不为缺乏经验的用户提供更多支持，则存在过度收敛的风险，导致不必要的更昂贵的计算，或者收敛不足，导致完全无用的计算，这是对计算资源和电力的浪费。保守估计，DFT计算的欠收敛或过收敛比例在10%范围内。由于在这项研究中投入了大量的计算资源，即使是相对较小的效率提高将导致大量减少浪费的计算资源，并显着改善研究基础设施的环境可持续性。该项目将产生一个工具和基于证据的最佳实践，以提供自动的，专家指导“金发姑娘”选择这些收敛参数。这将通过训练机器学习（ML）模型来实现，以预测DFT数值近似的收敛参数，从而达到常见类型科学研究所需的精度。鉴于需要收敛的数值近似存在于所有代码中，该工具将适用于英国常用的所有DFT代码。该项目的主要贡献将是大大提高效率和保证水平的负责任地使用UKRI和EPSRC的硬件和软件基础设施，现在和未来。比较计算资源的使用典型的作业运行之前和之后，采用这个工具，将使基线量化和外推的效率。该分析的结果将在全球范围内传播，从而在国际计算设施中产生最佳实践，从而在全球范围内扩大计算基础设施的环境可持续性方面的收益。该项目将是基于ML的DFT计算输入自动生成的重要一步，以及计算资源和碳足迹的自动先验计算器。这种自动化将有助于在世界部分地区使用这种研究方法的民主化，在这些地区，数字研究基础设施可能比实验设施更容易获得。