| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces |
| Author(s) |
Siddarth Achar, Derek A Stewart, Julian Schneider |
| On-Site Speaker (Planned) |
Siddarth Achar |
| Abstract Scope |
Chalcogenide alloys for selector and memory elements for next generation non-volatile memory cells may suffer from interdiffusion at interfaces due to Joule heating and high applied fields. This interdiffusion can degrade device performance over time. While first principles atomistic simulation can provide insight into the electronic structure and local atomic bonding configurations, this approach is limited to small atomic systems and time scales. To explore this problem on a broader scale, we developed machine-learning empirical potentials that can be used in molecular dynamic simulations of Ge-Se alloys interfaced with Ti electrodes. We used the moment-tensor approach with active learning (as implemented in QuantumATK) to construct these potentials, drawing on a dataset of ab-initio calculations for relevant Ge, Se, and Ti systems. We will discuss the evolution of the composition profile over time and the impact interdiffusion will have on the electronic properties of these device structures using our potentials. |