About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
2D Materials – Preparation, Properties, Modeling & Applications
|
| Presentation Title |
In silico Multiscale Design and Microstructure Evaluation of High-Mobility, Intrinsic p-Type Quasi-random 2D Boron Carbon Nitride |
| Author(s) |
Jui-Cheng Kao, Po-Yu Yang, Chun-Wei Pao |
| On-Site Speaker (Planned) |
Chun-Wei Pao |
| Abstract Scope |
Two-dimensional boron carbon nitride (2D BCN) promises to bridge the properties of graphene and h-BN, yet phase separation typically hinders the synthesis of intrinsic p-type BCN. In a close experiment-theory collaboration recently accepted by Nature, we introduce a multiscale computational workflow—integrating density functional theory (DFT), machine learning (ML), and Monte Carlo simulations—to rationalize the wafer-scale epitaxial growth of high-mobility (~100 cm²/V·s) p-type 2D BCN. We show that a dual-precursor strategy kinetically suppresses graphene segregation through gradual carbon release. ML-driven thermodynamic sampling reveals that non-stoichiometric, quasi-random BCN structures resist phase separation, validated by atomic-resolution imaging. Finally, DFT confirms that carbon substitution at nitrogen sites governs the 1.90 eV bandgap and p-type behavior. This tightly coupled approach provides a predictive blueprint for designing advanced 2D semiconductors. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Phase Transformations |