Unraveling Tellurium Defect‐Mediated Structural Phase Stability of Monolayer MoTe 2 Using Fine‐Tuned Machine‐Learning Interatomic Potentials

ABSTRACT Monolayer molybdenum ditelluride (MoTe 2 ) exhibits a small energy difference between its semiconducting 2H and semimetallic 1T’ phases, enabling structural phase transitions under external perturbations such as defects, strain and temperature. However, the combined thermodynamic effects of Te defects and lattice strain at finite temperatures remain insufficiently understood. In this work, the defect‐dependent phase stability of monolayer MoTe 2 is investigated using a machine‐learning interatomic potential (MLIP) fine‐tuned on density functional theory (DFT) training data. Helmholtz free‐energy calculations reveal that both Te vacancies and Te adatoms thermodynamically stabilize the 1T’ phase relative to the 2H phase. The critical defect concentration required for 1T’ phase stabilization decreases systematically with increasing temperature, indicating strong coupling between temperature and defect‐mediated phase stability. Additionally, strain and Te defects exhibit a pronounced synergistic effect: defects significantly reduce the critical strain required for the 2H‐1T’ phase transition, while strain lowers the defect concentration necessary for stabilization of the 1T’ phase. These findings provide new insight into defect‐mediated phase stability in MoTe 2 and establish a thermodynamic framework applicable to other transition metal dichalcogenide systems, offering practical guidance for phase‐controlled synthesis and device engineering.

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Publication Details

Journal
Advanced Theory and Simulations
Published
2026-10-29
DOI
https://doi.org/10.1002/adts.70596
Primary Topic
2D Materials and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Unraveling Tellurium Defect‐Mediated Structural Phase Stability of Monolayer MoTe 2 Using Fine‐Tuned Machine‐Learning Interatomic Potentials

Kuk-Song Choe, Aleksei G. Kozlov, Alexey Vyacheslavovich Ognev, Kuznetsova Mariya et al.
4 links
Advanced Theory and Simulations
2D Materials and Applications
article

Unraveling Tellurium Defect‐Mediated Structural Phase Stability of Monolayer MoTe 2 Using Fine‐Tuned Machine‐Learning Interatomic Potentials

Kuk-Song Choe, Aleksei G. Kozlov, Alexey Vyacheslavovich Ognev, Kuznetsova Mariya, Chol‐Min Pak, Ruslan A. Shestopalov
article en

Abstract

ABSTRACT Monolayer molybdenum ditelluride (MoTe 2 ) exhibits a small energy difference between its semiconducting 2H and semimetallic 1T’ phases, enabling structural phase transitions under external perturbations such as defects, strain and temperature. However, the combined thermodynamic effects of Te defects and lattice strain at finite temperatures remain insufficiently understood. In this work, the defect‐dependent phase stability of monolayer MoTe 2 is investigated using a machine‐learning interatomic potential (MLIP) fine‐tuned on density functional theory (DFT) training data. Helmholtz free‐energy calculations reveal that both Te vacancies and Te adatoms thermodynamically stabilize the 1T’ phase relative to the 2H phase. The critical defect concentration required for 1T’ phase stabilization decreases systematically with increasing temperature, indicating strong coupling between temperature and defect‐mediated phase stability. Additionally, strain and Te defects exhibit a pronounced synergistic effect: defects significantly reduce the critical strain required for the 2H‐1T’ phase transition, while strain lowers the defect concentration necessary for stabilization of the 1T’ phase. These findings provide new insight into defect‐mediated phase stability in MoTe 2 and establish a thermodynamic framework applicable to other transition metal dichalcogenide systems, offering practical guidance for phase‐controlled synthesis and device engineering.

Advanced Theory and SimulationsVol. 9(11)
Sakhalin State University (RU), Far Eastern Federal University (RU), Kim Chaek University of Technology (KP)
Russian Science Foundation, Far Eastern Federal University
Openalex Percentile: Top 29%
2D Materials and Applications
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