2023
DOI: 10.21203/rs.3.rs-3376755/v1
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Machine learning and atomistic origin of high dielectric permittivity in oxides

Yuho Shimano,
Alex Kutana,
Ryoji Asahi

Abstract: Discovering new stable materials with large dielectric permittivity is important for future energy storage and electronics applications. Theoretical and computational approaches help design new materials by elucidating microscopic mechanisms and establishing structure-property relations. Ab initio methods can be used to reliably predict the dielectric response, but for fast materials screening, machine learning (ML) approaches, which can directly infer properties from the structural information, are needed. He… Show more

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