| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Thermal Phenomena in Covalently Bonded Systems Modeled via Physically Informed Neural Network Potentials |
| Author(s) |
James F. Hickman, Ganga Purja Pun, Francesca Tavazza, Yuri Mishin |
| On-Site Speaker (Planned) |
James F. Hickman |
| Abstract Scope |
The present work focuses on the development and application of a novel class of interatomic potentials known as a “physically-informed neural network” (PINN) potentials. This potential format combines the high level of flexibility inherent to artificial neural networks (ANNs) with the transferability associated with physically inspired analytic potential models. Currently we focus on single component, covalently bonded systems including silicon (Si), germanium (Ge), and carbon (C), however, the PINN model can generally be applied to any multicomponent metallic or covalent system. The comparison between these newly developed force fields and existing classical and ANN potentials demonstrates the increased accuracy and transferability of the PINN model. Finally we discuss the application of these new potentials to the study of various thermal properties including thermal stability, expansion, and conductivity in low dimensional phases such as Silicene and Germanene. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |