About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Finite-temperature Lattice Dynamics from Graph Kernel Machine Learning Interatomic Potentials |
Author(s) |
Jorge A. Munoz, Adrian De la Rocha, Valeria Arteaga, Vanessa Meraz, Sofia Gomez, Yu-Hang Tang, Wibe de Jong |
On-Site Speaker (Planned) |
Jorge A. Munoz |
Abstract Scope |
A description of the potential energy surface as a function of atomic configuration is required for atomistic simulations of materials. Descriptions based on density functional theory (DFT) are accurate but computationally expensive. Machine learning interatomic potentials emerged in the past decade and can now achieve DFT-like accuracy while being much cheaper. We present a framework that uses Gaussian process regression to predict the energy of a crystalline solid when its atoms are displaced from their equilibrium positions based on how similar the atomic configurations are to each other; similarity is computed using the marginalized graph kernel. The prediction error is sufficiently small to extract the finite-temperature lattice dynamics using as few as 300 atomic configurations for training, which are found via active learning. Forces and force constants can be obtained directly from the potential using automatic differentiation. We compute the high-temperature lattice dynamics of bcc Zr to demonstrate the framework. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, High-Temperature Materials |