Open Access Article
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Correction: Beyond training data: how elemental features enhance ML-based formation energy predictions
Hamed
Mahdavi
*a,
Vasant
Honavar
a and
Dane
Morgan
b
aDepartment of Computer Science and Engineering, Pennsylvania State University, State College, PA 16801, USA. E-mail: hmm5834@psu.edu; vuh14@psu.edu
bDepartment of Material Science and Engineering, University of Wisconsin Madison, Wisconsin WI 53706, USA. E-mail: ddmorgan@wisc.edu
First published on 31st October 2025
Abstract
Correction for “Beyond training data: how elemental features enhance ML-based formation energy predictions” by Hamed Mahdavi et al., Digital Discovery, 2025, 4, 2972–2982, https://doi.org/10.1039/D5DD00182J.
In the original version of the article the DOI link to the code, relevant scripts, and data deposited in the Mendeley repository for this article in the Data availability statement was not present. An updated data availability statement can be found here. Data availability
Our experiments used the Matbench v0.1 test suite, publicly accessible via the Matminer (https://url.uk.m.mimecastprotect.com/s/Tp92CK1VQF4zm16uMf1u5fpWW?domain=hackingmaterials.lbl.gov) Python library. The complete implementation of the experiments—including code, scripts, and data—is available in the paper's Mendeley repository: https://doi.org/10.17632/n3cwj2hb7w.2.
Supplementary information is available. See DOI: https://doi.org/10.1039/d5dd00182j.
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.| This journal is © The Royal Society of Chemistry 2025 |