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

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

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 initi...

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Bibliographic Details
Main Authors: Alex Kutana, Koki Yoshimochi, Ryoji Asahi
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Science and Technology of Advanced Materials: Methods
Subjects:
Graph neural network
machine learning
dielectrics
perovskite oxides
phase transitions
Online Access:https://www.tandfonline.com/doi/10.1080/27660400.2025.2497254
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https://www.tandfonline.com/doi/10.1080/27660400.2025.2497254

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