Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned?

While artificial intelligence is promoting remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate the environmental impacts of deep learning in materials science through extensive benchmarking. Specifically, a diverse set of neural networks is trained for a particular supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective shows diminishing returns, which are expressed in a 28% decrease in mean absolute error and nearly a 15000% increase in carbon footprint of model training in 2016–2022. By utilizing up-to-date graphics processing units, it is possible to partially offset the immense growth in GHG emissions. Nonetheless, the practice to employ energy-efficient hardware is unaddressed by the materials informatics community, as it follows from the literature analysis in the field. Based on our findings, we encourage researchers to report GHG emissions on par with standard performance metrics.