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Text-Embedding-Assisted Design of Rigid Molecular Cations for Suppressing Ion Migration in Hybrid Single-Crystal
Pengda Tong1, Chenyang Yu2, Yawen Ouyang3
1Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education; Shaanxi Key Laboratory for Advanced Energy Devices; Shaanxi Engineering Lab for Advanced Energy Technology; International Joint Research Center of Shaanxi Province for Photoelectric Materials Science; Institute for Advanced Energy Materials; School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, China.
Researchers developed a machine-learning model to enhance hybrid crystal stability. Replacing flexible organic cations with rigid ones significantly reduced ion migration, improving performance in X-ray detectors.
Area of Science:
- Materials Science
- Solid-State Physics
- Optoelectronics
Background:
- Hybrid single crystals offer potential for photovoltaics and radiation detection.
- Ion migration in these materials causes instability, hindering commercial use.
Purpose of the Study:
- To develop a machine-learning approach for identifying stable hybrid crystal structures.
- To enhance the stability and performance of hybrid semiconductors by controlling ion migration.
Main Methods:
- Integrated large language models (LLMs) with k-Nearest Neighbor (kNN) algorithms to predict cation effects on crystal stiffness.
- Synthesized (CHDA)BiI5 single crystals by replacing flexible alkyl chains with rigid carbon rings.
- Employed Density Functional Theory (DFT) and solid-state nuclear magnetic resonance (SSNMR) for structural and dynamic analysis.
Main Results:
- Identified rigid organic cations as key to suppressing ion migration and enhancing lattice rigidity.
- Demonstrated increased ion migration activation energy from 0.42 to 0.61 eV in (CHDA)BiI5.
- Achieved high sensitivity (8209 μC·Gyair−1·cm−2) and low detection limit (4.7 nGy s−1) in (CHDA)BiI5 X-ray detectors.
Conclusions:
- Organic cation rigidity is crucial for optimizing the stability and performance of low-dimensional hybrid semiconductors.
- The developed machine-learning strategy effectively guides the design of stable hybrid materials.
- This work paves the way for more robust and efficient optoelectronic and radiation detection devices.
