| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential |
| Author(s) |
Qiang Zhu, Pedro Santos, Yansun Yao |
| On-Site Speaker (Planned) |
Qiang Zhu |
| Abstract Scope |
In this work, we introduce the construction of machine learning potential (MLP) for the purpose of solid-solid phase transition studies. We will explain how to generate the high quality training data from quantum mechanical (QM) simulation to adequately describe the potential energy surface between multiple solid forms. The data is then used to train the accurate MLP based our recently developed NN-SNAP scheme. Using the GaN as a model system, we apply the newly developed MLP to investigate the B1-B4 phase transition under high pressure based on metadynamics simulation. Varying the size of starting models, we can clearly identify both homogeneous and hetrogenous nucleation mechanisms that cannot be accounted from the past studies based on QM simulation. |