2 papers
cond-mat.mtrl-sci2024
Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
Alex Kutana, Koki Yoshimochi, Ryoji Asahi
Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Mach…
cond-mat.mtrl-sci2024
Representing Born effective charges with equivariant graph convolutional neural networks
Alex Kutana, Koji Shimizu, Satoshi Watanabe +1
Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-…