Peer-reviewedCC BY 4.0Scientific Reports
Identifiability assessment and physics-informed residual machine learning for calculated Ti–Zr–Nb–Ta–X thermodynamics (X = Mo, Hf)
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BibTeX
@article{yasin2026identifiability,
title = {{Identifiability assessment and physics-informed residual machine learning for calculated Ti–Zr–Nb–Ta–X thermodynamics (X = Mo, Hf)}},
author = {Mohamed Yasin and Abid Yahya and Lory Liza D. Bulay-Og and Nathaniel E. Quimada and Dionel O. Albina and John Rey B. Bajuyo and Yar Muhammad and Eva Assiimwe and Irfan Anjum Badruddin},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-78138-5},
url = {https://www.nature.com/articles/s41598-026-78138-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identifiability assessment and physics-informed residual machine learning for calculated Ti–Zr–Nb–Ta–X thermodynamics (X = Mo, Hf)
AU - Mohamed Yasin
AU - Abid Yahya
AU - Lory Liza D. Bulay-Og
AU - Nathaniel E. Quimada
AU - Dionel O. Albina
AU - John Rey B. Bajuyo
AU - Yar Muhammad
AU - Eva Assiimwe
AU - Irfan Anjum Badruddin
PY - 2026
DA - 2026/11/02
JO - Scientific Reports
DO - 10.1038/s41598-026-78138-5
UR - https://www.nature.com/articles/s41598-026-78138-5
SN - 2045-2322
ER -