Navigating the "Kessel Run" of digital materials acceleration.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 36419443.
- Also identified by DOI 10.1016/j.patter.2022.100638 and PMC identifier 9676519.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Computational methods such as machine learning, artificial intelligence, and big data in physical sciences, particularly materials science, have been exponentially growing in terms of progress, method development, and number of studies and related publications. This aggregate momentum of the community is palpable, and many exciting discoveries are likely on the horizon. But, like all endeavors, some thought should be given to the current trajectory of the field, ensuring the full potential of the new digital space.