摘要
Dielectrics are crucial for technologies like flash memory,CPUs,photovoltaics,and capacitors,but public data on these materials are scarce,restricting research and development.Existing machine learning models have focused on predicting scalar polycrystalline dielectric constants,neglecting the directional nature of dielectric tensors essential for material design.This study leverages multi-rank equivariant structural embeddings from a universal neural network potential to enhance predictions of dielectric tensors.We develop an equivariant readout decoder to predict total,electronic,and ionic dielectric tensors while preserving O(3)equivariance,and benchmark its performance against stateof-the-art algorithms.
基金
supported by Preferred Networks Inc.
