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Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study

by Independent / Community 000bb2357732456d11d4f88f0678e226af653f86

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Abstract We reveal high-fidelity new phase selection rules for high entropy alloys (HEAs) by combining CALPHAD calculations and the machine learning (ML) method. Employing Thermo-Calc and TCHEA3 database, we first generate more than 300,000 equilibrium phase data from 20 quinary families formed by the 8 elements of Al Co, Cr, Cu, Fe, Mn, Ni, and Ti, and choose initially 15 materials/physical descriptors. The eXtreme Gradient Boosting (XGBoost) method is then used to identify 5 most important ...

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Registry ID	000bb2357732456d11d4f88f0678e226af653f86
License	ArXiv
Provider	semantic_scholar

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@misc{000bb2357732456d11d4f88f0678e226af653f86,
  author = {Unknown},
  title = {Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/000bb2357732456d11d4f88f0678e226af653f86}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study [Paper]. Free2AITools. https://api.semanticscholar.org/000bb2357732456d11d4f88f0678e226af653f86

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 87

Popularity (P) 63

Recency (R) 100

Quality (Q) 65

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FNI V2.0 for Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study: Authority (A:87), Popularity (P:63), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Abstract We reveal high-fidelity new phase selection rules for high entropy alloys (HEAs) by combining CALPHAD calculations and the machine learning (ML) method. Employing Thermo-Calc and TCHEA3 database, we first generate more than 300,000 equilibrium phase data from 20 quinary families formed by the 8 elements of Al Co, Cr, Cu, Fe, Mn, Ni, and Ti, and choose initially 15 materials/physical descriptors. The eXtreme Gradient Boosting (XGBoost) method is then used to identify 5 most important ..."

❝ Cite Node

@article{Unknown2026Revealing,
  title={Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📈106CitationsSemantic Scholar

🏛️87AuthorityFNI pillar

⏱️100RecencyFNI pillar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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citations: 106

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