Curation before creation: guided sampling for greener high-performance computing

Materials discovery and characterisation was revolutionised by computational science. The cost savings, improved efficiencies, and deeper scientific insights gained from computer simulations have supported pioneering developments in solar cells, computer chips, recyclable materials, sensing technologies, catalysts, and drug discovery. The global impact of these developments is indisputable. But, when evaluating the societal benefits of computational science, there is still a fly in the ointment: the direct monetary and environmental costs of high-performance computing (HPC). Green computing efforts typically focus on optimized computer architecture and software development. However. in this work we assume those environmental and general costs of HPC are fixed and aim to maximize how those resources by benchmarking guided sampling and active learning methods to direct which computational experiments are conducted. To achieve this, we used existing publicly available data sets and where appropriate benchmark improved model performance against the cost to acquire data. We undertook a meta-analysis of computational materials science and chemistry projects supported by the National Computing Merit Allocation Scheme (NCMAS) from 2019- 21 which encompassed 25 projects. We assessed data availability and provenance from 94 related papers and 39 datasets. We compared cross-domain informed sampling methods that prioritise selection based on with random, quasi-random, diversity, uniformity, and representativeness metrics. These were implemented in an active learning environment and performance assessed with learning curves.