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，例如绿色计算、环境修复以及能源生产、转换和存储。基于 DFT 的研究目前消耗了全球超级计算机上的大量资源。在英国，DFT 计算使用超过 45% 的 ARCHER2（英国一级国家超级计算服务）。 DFT 在 Tier2 系统和较低层机构计算机的使用中也占有重要地位。随着功能越来越强大的计算机的出现，基于 DFT 的研究对环境的影响正在迅速增加。提高研究效率并开发确保能源密集型计算资源得到负责任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项研究所需的精度进行收敛的数值近似值。如果没有对经验不足的用户提供更多支持，则存在的风险是过度收敛，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，浪费计算资源和电力。保守估计 DFT 计算收敛不足或过度的比例在 10% 范围内。鉴于本研究投入了大量的计算资源，即使效率相对较小的提高也会导致计算资源浪费的大量减少，并显着改善研究基础设施的环境可持续性。该项目将产生一种工具和证据基于最佳实践，为“金发姑娘”选择这些收敛参数提供自动、专家指导。这将通过训练机器学习 (ML) 模型来预测 DFT 数值近似的收敛参数来实现，以满足常见类型科学研究所需的精度。鉴于所有代码中都存在需要收敛的数值近似，因此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现在和将来负责任地使用 UKRI 和 EPSRC 硬件和软件基础设施的效率和保证水平。采用之前和之后运行的典型作业的计算资源使用情况比较该工具将能够对所获得的效率进行基线量化和推断。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形成最佳实践，从而在全球范围内扩大计算基础设施环境可持续性的收益。该项目将是迈向基于机器学习自动生成输入的重要一步DFT 计算，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上一些地方使用这种研究方法的民主化，这些地方的数字研究基础设施可能比实验设施更容易获得。