A5010639121- Machine Learning in Materials Science
- Ion-surface interactions and analysis
- Nuclear Materials and Properties
- Graphite, nuclear technology, radiation studies
- Advanced Semiconductor Detectors and Materials
- Silicon and Solar Cell Technologies
- Fiber-reinforced polymer composites
- Semiconductor materials and devices
- Nuclear materials and radiation effects
- Polymer Nanocomposite Synthesis and Irradiation
- Thin-Film Transistor Technologies
- Advanced Memory and Neural Computing
Harbin Institute of Technology
2022-2025
Heilongjiang Institute of Technology
2022-2024
Epoxy resins are critical materials in aerospace applications, yet their mechanical properties, specifically the tensile modulus, can be significantly compromised when exposed to electron irradiation space environments. To thoroughly examine this degradation, we developed an integrated research approach combining vacuum experiments with multi-scale simulations. Coarse-grained (CG) and Monte Carlo (MC) methods were employed generate necessary models primary knock-on atom (PKA) data, while...
We developed an accurate and efficient machine learning potential with DFT accuracy applied it to the silicon dry/wet oxidation process investigate underlying physics of thermal (001) surfaces. The was verified by comparing melting point structural properties silicon, a-SiO2, adsorption on surface experiment data. In subsequent simulations, we successfully reproduced accelerated growth phenomenon wet in experiment, discussed oxide detail, elucidated that is due hydrogen system both enhances...