From Synthesis to Machine Learning Validation for the Thermoelectric Zintl Phase Ba1-xEuxZn2-yAlySb2 System

Yunjeong Lee; Aziz Ahmed; Jihyun Lee; Tae‐Soo You; Kang Min Ok

doi:10.1039/d5ta08713a

January 1, 2026Journal of Materials Chemistry A

From Synthesis to Machine Learning Validation for the Thermoelectric Zintl Phase Ba1-xEuxZn2-yAlySb2 System

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Key Points

The aim is to synthesize new Zintl phase compounds in the Ba1-xEuxZn2-yAlySb2 system for thermoelectric applications and validate them using machine learning.
Synthesized compounds using the molten Pb-flux method.
Characterized the compounds' crystallization using orthorhombic BaCu₂S₂-type phase analysis.
Applied machine learning techniques for validation of synthesis outcomes.
Five new Zintl phase compounds were successfully synthesized.
All compounds crystallized in the orthorhombic phase.
Machine learning validation supports the efficacy of the synthesis method.

Implication

Research explores synthesis and machine learning validation of new thermoelectric compounds, indicating potential applications.

Abstract

Five new Zintl phase compounds in the Ba1-xEuxZn2-yAlySb2 system were synthesized using the molten Pb-flux method for thermoelectric application. All compounds crystallized in the orthorhombic BaCu₂S₂-type phase with a phase...

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Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/696b2631d2a12237a93496c5 https://doi.org/10.1039/d5ta08713a

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From Synthesis to Machine Learning Validation for the Thermoelectric Zintl Phase Ba1-xEuxZn2-yAlySb2 System

Key Points

Implication

Abstract

Discussion

Cite This Study

From Synthesis to Machine Learning Validation for the Thermoelectric Zintl Phase Ba1-xEuxZn2-yAlySb2 System

Key Points

Implication

Abstract

Discussion

Cite This Study

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