Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms
arXiv:1912.04596 · doi:10.1103/PhysRevMaterials.4.113602
Abstract
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach and show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.
References in corpus (7)
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- On-the-fly machine learning force field generation: Application to melting points
- Machine-learning based interatomic potential for amorphous carbon
- Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
- Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
- De novo exploration and self-guided learning of potential-energy surfaces
- Nanoscale-hydride formation at dislocations in palladium: Ab initio theory and incoherent inelastic neutron scattering measurements