| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Development of a Machine Learned Interatomic Potential for Shock Simulations of Boron Carbide |
| Author(s) |
Kimia Ghaffari, Salil Bavdekar, Douglas Spearot, Ghatu Subhash |
| On-Site Speaker (Planned) |
Kimia Ghaffari |
| Abstract Scope |
Due to recent unprecedented innovations in computing power, data-driven methods like Machine Learning (ML) have risen in popularity in the field of solid mechanics. Specifically, ML models have been fit to material potential energy surfaces (PES) due to their ability to reach ab initio accuracy with significantly reduced computational cost. Neural Networks (NNs) are flexible learning methods that learn the PES of complex materials. This work details the development of an NN-based IP for boron carbide (B4C), specifically the training data generation, model selection, and model validation. The breadth of computational and experimental literature available on B4C allows for development and thorough validation of the model. Preliminary results indicate higher accuracy and a 10-fold computation speed increase in NN-based IP shock simulations as compared to traditional IP simulations. This increase in efficiency can radically improve the predictability and accuracy of computational investigations previously unattainable with conventional approach. |