Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A Call for Caution in the Era of AI-Accelerated Materials Science

Published

Author(s)

Kangming Li, Edward Kim, Yao Fehlis, Daniel Persaud, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers

Abstract

It is safe to state that the field of matter has successfully entered the fourth paradigm, where machine learning and artificial intelligence (AI) are universally seen as useful, if not truly intelligent. AI's utilization is near-ubiquitous from the prediction of novel materials to reducing computational overhead for material simulations; its value has been demonstrated time and again by both theorists and experimentalists. There is, however, a worrying trend toward large datasets and overparameterized models being all we need to accelerate science through accurate and robust machine learning systems.
Citation
Matter
Volume
6
Issue
12

Citation

LI, K. , Kim, E. , Fehlis, Y. , Persaud, D. , DeCost, B. , Greenwood, M. and Hattrick-Simpers, J. (2023), A Call for Caution in the Era of AI-Accelerated Materials Science, Matter, [online], https://doi.org/10.1016/j.matt.2023.10.027, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956736 (Accessed October 7, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created December 6, 2023, Updated September 27, 2024