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Zhu, Siya; Sarıtürk, Doğuhan; Arróyave, Raymundo (2025) Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01913-x

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Reference TypeJournal (article/letter/editorial)
TitlePublisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy
Journalnpj Computational Materials
AuthorsZhu, SiyaAuthor
Sarıtürk, DoğuhanAuthor
Arróyave, RaymundoAuthor
Year2025 (December 19)Volume11
Issue1
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1038/s41524-025-01913-xSearch in ResearchGate
Generate Citation Formats
Mindat Ref. ID19379333Long-form Identifiermindat:1:5:19379333:2
GUID0
Full ReferenceZhu, Siya; Sarıtürk, Doğuhan; Arróyave, Raymundo (2025) Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01913-x
Plain TextZhu, Siya; Sarıtürk, Doğuhan; Arróyave, Raymundo (2025) Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01913-x
In(2025, January) npj Computational Materials Vol. 11 (1). Springer Science and Business Media LLC

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Zhu, Siya; Sarítürk, Doğuhan; Arróyave, Raymundo (2025) Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01814-z
Poul, Marvin; Huber, Liam; Neugebauer, Jörg (2025) Automated generation of structure datasets for machine learning potentials and alloys. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01669-4
Corradini, Andrea; Marini, Giovanni; Calandra, Matteo (2025) Author Correction: Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01748-6
Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752
Zhang, Chenbo; Chen, Xian (2025) FerroAI: a deep learning model for predicting phase diagrams of ferroelectric materials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01778-0
Cao, Yifan; Sheriff, Killian; Freitas, Rodrigo (2025) Capturing short-range order in high-entropy alloys with machine learning potentials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01722-2
Corradini, Andrea; Marini, Giovanni; Calandra, Matteo (2025) Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01614-5
Vondrák, Martin; Reuter, Karsten; Margraf, Johannes T. (2025) Pushing charge equilibration-based machine learning potentials to their limits. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01791-3
Sheikh, Sofia; Vela, Brent; Honarmandi, Pejman; Morcos, Peter; Shoukr, David; Kotb, Abdelrahman Mostafa; Karaman, Ibrahim; Elwany, Alaa; Arróyave, Raymundo (2025) High-throughput alloy and process design for metal additive manufacturing. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01670-x
Belli, Francesco; Zurek, Eva (2025) Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01553-1
Zhu, Siya; Eckert, Hagen; Curtarolo, Stefano; Schroers, Jan; Arróyave, Raymundo; van de Walle, Axel (2026) Computational study of density fluctuation-facilitated shear band formation in bulk metallic glasses. npj Computational Materials, 12 (1). doi:10.1038/s41524-026-02031-y
 
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To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
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