The carbon footprint of predicting CO₂ storage capacity in metal-organic frameworks within neural networks.

ABSTRACT: While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15,000% increase in the carbon footprint of model training in 2016-2022. By means of up-to-date graphics processing units, it is possible to partially offset the immense growth of GHG emissions. Nonetheless, the practice of employing ene

SUBMITTER: Korolev V 

PROVIDER: S-EPMC11019266 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature