| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Dynamic Behavior of Materials X
|
| Presentation Title |
Shock Compression of Nanocrystalline Boron Carbide from Deep Learning Molecular Dynamics Simulations |
| Author(s) |
Qi An, Jun Li |
| On-Site Speaker (Planned) |
Qi An |
| Abstract Scope |
Understanding the dynamic behaviors of strong ceramics under shock compression is critical for their applications in extreme environments. Here, we develop an accurate machine learning force field (ML-FF) for superhard B4C by training deep neutral network using quantum mechanics simulations. Then we apply this ML-FF to examine the shock response and associated deformation mechanisms of nanocrystalline boron carbide (n-B4C) using large-scale non-equilibrium molecular dynamics simulations. The simulation results suggest that the grain boundary (GB) sliding and amorphization are responsible for the propagation of quasiplastic waves above its Hugoniot elastic limit. At high shock strength, the disintegrated icosahedra initiating from GBs propagate towards internal grains, causing the intragranular amorphous band formation. Our simulation results on shock Hugoniot agree very well with previous experiments. This study explains the shock-induced quasiplastic behaviors of nanocrystalline B4C, providing significant insight into assessing the deformation and damage of nanocrystalline ceramics under shock loading. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Mechanical Properties, Mechanical Properties |