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

31 July 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

carbon footprint
greenhouse gas emissions
artificial intelligence
neural networks
materials informatics

Supplementary materials

Title
Description
Actions
Title
Supporting Information
Description
Figures S1-S3
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.