Correction: Beyond training data: how elemental features enhance ML-based formation energy predictions

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.

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Journal
Digital Discovery
Published
2025-01-01
DOI
https://doi.org/10.1039/d5dd90047f
Primary Topic
Machine Learning in Materials Science
Type
article
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0.00
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Correction: Beyond training data: how elemental features enhance ML-based formation energy predictions

Vasant Honavar, Dane Morgan, Hamed Mahdavi
Digital Discovery
Machine Learning in Materials Science
article

Correction: Beyond training data: how elemental features enhance ML-based formation energy predictions

Vasant Honavar, Dane Morgan, Hamed Mahdavi
article en

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.

Digital DiscoveryVol. 4(12)
University of Wisconsin System (US), Pennsylvania State University (US), University of Wisconsin–Madison (US)
Openalex Percentile: Top 75%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Correction: Beyond training data: how elemental features enhance ML-based formation energy predictions — Vasant Honavar, Dane Morgan, et al. · Digital Discovery (2025) | TGRS Research Map | TGRS