| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
Neural Network Potential for Lattice Dynamics Calculations and Thermal Conductivity Prediction |
| Author(s) |
Jie Gong, Hyun-Young Kim, Alan J. H. McGaughey |
| On-Site Speaker (Planned) |
Jie Gong |
| Abstract Scope |
Lattice dynamics calculations can be used to predict the phonon properties of insulating and semi-conducting crystals. These calculations require force constants, which can be found using density functional theory (DFT). The force constants of simple materials (high symmetry and small primitive cell) can be found with relatively few DFT calculations, but this number increases significantly for more complex materials, taxing computational resources.
We address this issue by training a high-dimensional neural network potential to calculate force constants with a small training set. An adaptive selection scheme is used to select the training data efficiently. Using silicon, we quantify the accuracy using the phonon frequencies and thermal conductivity, in addition to the standard force and energy metrics. We find that accurate forces, energies, and frequencies do not guarantee an accurate thermal conductivity. A single training set and hyperparameters can result in a range of thermal conductivities. |