| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning |
| Author(s) |
Ajay Annamareddy |
| On-Site Speaker (Planned) |
Ajay Annamareddy |
| Abstract Scope |
Studying the kinetics in the glassy state, especially of an aged glass, becomes computationally prohibitive with molecular dynamics simulations because of the slowdown in dynamics. We aim to integrate kinetic Monte Carlo with machine learning (ML) to study the dynamics and diffusion in the aged state. This requires that ML methods be trained to accurately predict the hop-rate and hop-vector from a local description of atom. The hop-rate is accessible from softness developed by Andrea Liu and collaborators. Initial studies on how much hop-vectors can vary given the same environment indicates that the same starting conditions lead to hops clustered closely together in direction, supporting that hop-vector is approximately controlled by local environment and can be machine-learned. To predict the hop-vector, we adopted existing schemes used in ML interatomic potentials to define a local coordinate system and a rotationally-invariant feature vector and now using a deep learning convolutional neural network. |