Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials

ABSTRACT Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Machine learning (ML) and artificial intelligence (AI) address this issue by enabling accurate models with close to ab initio accuracy. Here, we demonstrate the utility of ML models in capturing properties of realistic materials by performing finite temperature molecular dynamics simulations of perovskite oxides using a force field based on equivariant graph neural networks. The models demonstrate efficient learning from a small training dataset of energies, forces, stresses, and tensors of Born effective charges. We qualitatively capture the temperature dependence of the dielectric tensor and structural phase transitions in calcium titanate. GRAPHICAL ABSTRACT IMPACT STATEMENT The newly developed hybrid GNN model to predict a force field and Born effective charges successfully enables to simulate temperature-dependent anisotropic dielectric tensor from finite temperature MD simulations.