Steric‐Pressure‐Driven Topological Reconstruction for High‐Performance Zintl Phase Thermoelectrics with Machine‐Learning Validation
- 1Department of Chemistry Chungbuk National University Cheongju Chungbuk Republic of Korea
- 2Department of Chemistry Sogang University Seoul Republic of Korea
- 3Department of Materials Science and Engineering Yonsei University Seoul Republic of Korea
Abstract
The Zintl phase A9M4+
x Pn9 (A = Ca, Sr, Eu, Yb; M = transition metal; Pn = pnictogen) family is a promising class of thermoelectric (TE) materials, but conventional optimization has relied on interstitial site tuning rather than structural reconstruction. In this work, we achieve a steric‐pressure‐driven topological transformation in the quinary Ca9−
x Sr x Zn4+
y
−
z Ag z Sb9 system through synergistic Sr and Ag co‐doping. Single‐crystal X‐ray diffraction reveals that substituting larger Sr2+ for Ca2+ progressively drives Zn migration, transforming the parent 2‐dimensional (2D) zigzag‐shaped anionic layers into a cross‐linked 3D polyanionic framework. Density functional theory calculations indicate that Ag incorporation preserves the pseudogap near the Fermi level and reduces bond polarity, optimizing the electronic structure for hole transport. Together with the reconstructed 3D framework, these electronic effects significantly enhance carrier mobility and electrical conductivity without sacrificing a large Seebeck coefficient. As a result, the fully transformed quinary compound Ca7.59(2)Sr1.41Zn4.37(2)Ag0.19Sb9 achieves a maximum ZT of 0.73 at 873 K, doubling the performance of the parent ternary Ca9Zn4.5Sb9. A hybrid transfer machine‐learning approach (CrabNet and XGBoost) validates compositional trends and identifies the high‐performance region. These findings demonstrate that steric‐pressure‐induced 2D‐to‐3D framework reconstruction coupled with electronic optimization offers an effective design principle for high‐performance Zintl TE materials.
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BibTeX
@article{kim2026stericpressuredriven,
title = {{Steric‐Pressure‐Driven Topological Reconstruction for High‐Performance Zintl Phase Thermoelectrics with Machine‐Learning Validation}},
author = {Siyeon Kim and Aziz Ahmed and Juwon Lee and Jihyun Lee and Ji Hee Pi and Kyu Hyoung Lee and Kang Min Ok and Tae‐Soo You},
journal = {Advanced Science},
year = {2026},
month = oct,
doi = {10.1002/advs.78519},
url = {https://doi.org/10.1002/advs.78519},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Steric‐Pressure‐Driven Topological Reconstruction for High‐Performance Zintl Phase Thermoelectrics with Machine‐Learning Validation
AU - Siyeon Kim
AU - Aziz Ahmed
AU - Juwon Lee
AU - Jihyun Lee
AU - Ji Hee Pi
AU - Kyu Hyoung Lee
AU - Kang Min Ok
AU - Tae‐Soo You
PY - 2026
DA - 2026/10/26
JO - Advanced Science
DO - 10.1002/advs.78519
UR - https://doi.org/10.1002/advs.78519
AB - The Zintl phase A9M4+ x Pn9 (A = Ca, Sr, Eu, Yb; M = transition metal; Pn = pnictogen) family is a promising class of thermoelectric (TE) materials, but conventional optimization has relied on interstitial site tuning rather than structural reconstruction. In this work, we achieve a steric‐pressure‐driven topological transformation in the quinary Ca9− x Sr x Zn4+ y − z Ag z Sb9 system through synergistic Sr and Ag co‐doping. Single‐crystal X‐ray diffraction reveals that substituting larger Sr2+ for Ca2+ progressively drives Zn migration, transforming the parent 2‐dimensional (2D) zigzag‐shaped anionic layers into a cross‐linked 3D polyanionic framework. Density functional theory calculations indicate that Ag incorporation preserves the pseudogap near the Fermi level and reduces bond polarity, optimizing the electronic structure for hole transport. Together with the reconstructed 3D framework, these electronic effects significantly enhance carrier mobility and electrical conductivity without sacrificing a large Seebeck coefficient. As a result, the fully transformed quinary compound Ca7.59(2)Sr1.41Zn4.37(2)Ag0.19Sb9 achieves a maximum ZT of 0.73 at 873 K, doubling the performance of the parent ternary Ca9Zn4.5Sb9. A hybrid transfer machine‐learning approach (CrabNet and XGBoost) validates compositional trends and identifies the high‐performance region. These findings demonstrate that steric‐pressure‐induced 2D‐to‐3D framework reconstruction coupled with electronic optimization offers an effective design principle for high‐performance Zintl TE materials.
SN - 2198-3844
ER -