Apr 11, 2025
9:00am - 9:30am
Summit, Level 4, Room 423
Ryoji Asahi1,Alexander Kutana1
Nagoya University1
High dielectric permittivity materials are the key component in capacitive electronic and high electric power density applications and devices. Besides the required high relative dielectric permittivity, the desired properties for such materials include temperature and electric field stability, low dielectric losses, and high breakdown voltage. Most of the presently known materials with high permittivity do not meet all of these conditions, limiting applications. On the other hand, in In-Nb co-doped rutile TiO
2, permittivity boosting to >10
4 was reported, suggesting that it is possible to intrinsically improve the dielectric constant of bulk crystals by controlling the complex defect structure [1,2].
The dielectric properties are evaluated by state-of-the-art density functional perturbation theory, which is rather computationally demanding. In addition, complexity of dielectric properties including doping effects, temperature effects, and phase transitions makes us difficult to perform systematic or high-throughput evaluation of wide-range of dielectric materials.
In this study, we employ graph convolutional neural network (GNN) model to predict dielectric constants in large and complex material systems. Using this GNN model, the "local dielectric constants" are assigned at each atomic site in the network layer, indicating that the GNN model captures the features of the local structure, for example, modified by doping. To calculate the dependence on temperature and doping concentration, it is necessary to evaluate the statistical average using a large-scale model. To this end, we have developed an algorithm combining Monte Carlo sampling and the GNN model. In particular, the newly developed rotationally equivariant GNN model accurately predicts atomic Born effective charge and dielectric tensors [3]. The proposed method demonstrates accurate evaluation of temperature dependency of dielectric properties.
References(1) Kutana, Shimano, Asahi,
Sci. Rep. 13, 3761 (2023).
(2) Shimano, Kutana, Asahi,
Sci. Rep. 13, 22236 (2023).
(3) Kutana, Shimizu, Watanabe, Asahi, arXiv:2409.08940