Corrigendum to “Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm” [Appl. Surf. Sci. 612 (2023) 155734] (Applied Surface Science (2023) 612, (S0169433222032627), (10.1016/j.apsusc.2022.155734))

Abraham Burleigh, Miu Lun Lau, Megan Burrill, Daniel T. Olive, Jonathan G. Gigax, Nan Li, Tarik A. Saleh, Frederique Pellemoine, Sujit Bidhar, Min Long, Kavin Ammigan, Jeff Terry

Research output: Contribution to journalComment/debate

Abstract

The authors regret the omission of the following from the acknowledgements: Undergraduate student researcher (MB) was supported by the National Science Foundation – Research Experience for Undergraduates under award DMR 2050916. The authors would like to apologise for any inconvenience caused.

Original languageEnglish
Article number156840
JournalApplied Surface Science
Volume620
DOIs
StatePublished - 30 May 2023

Fingerprint

Dive into the research topics of 'Corrigendum to “Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm” [Appl. Surf. Sci. 612 (2023) 155734] (Applied Surface Science (2023) 612, (S0169433222032627), (10.1016/j.apsusc.2022.155734))'. Together they form a unique fingerprint.

Cite this