Projects
Projects: Projects for Investigator |
||
| Reference Number | EP/Z530657/1 | |
| Title | Goldilocks convergence tools and best practices for numerical approximations in Density Functional Theory calculations | |
| Status | Started | |
| Energy Categories | Energy Efficiency(Industry) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 20%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 80%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 95%; Other (Energy technology information dissemination) 5%; |
|
| Principal Investigator |
Dr B Montanari No email address given Scientific Computing Department STFC (Science & Technology Facilities Council) |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 January 2024 | |
| End Date | 31 December 2026 | |
| Duration | 36 months | |
| Total Grant Value | £378,233 | |
| Industrial Sectors | No relevance to Underpinning Sectors | |
| Region | South East | |
| Programme | NC : Infrastructure | |
| Investigators | Principal Investigator | Dr B Montanari , Scientific Computing Department, STFC (Science & Technology Facilities Council) (99.998%) |
| Other Investigator | Dr A Elena , Scientific Computing Department, STFC (Science & Technology Facilities Council) (0.001%) Dr S Basak , Scientific Computing Department, STFC (Science & Technology Facilities Council) (0.001%) |
|
| Web Site | ||
| Objectives | ||
| Abstract | 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 | |
| Publications | (none) |
|
| Final Report | (none) |
|
| Added to Database | 14/02/24 | |
