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  • Open Access

Physics-Based Factorized Machine Learning for Predicting Ionic Dielectric Tensors

Atsushi Takigawa1,2,*, Shin Kiyohara1,*,†, and Yu Kumagai1,3,‡

  • *These authors contributed equally to this work.
  • †Contact author: sin@tohoku.ac.jp
  • ‡Contact author: yukumagai@tohoku.ac.jp
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Phys. Rev. X 16, 021006 – Published 7 April, 2026

DOI: https://doi.org/10.1103/28wr-w896

Abstract

Considerable effort continues to be devoted to the exploration of next-generation high-κ materials that combine a high dielectric constant with a wide band gap. However, machine learning (ML)-based virtual screening has remained challenging, primarily due to the low accuracy in predicting the ionic contribution to the dielectric tensor, which dominates the dielectric performance of high-κ materials. We propose a joint ML model that predicts Born effective charges using an equivariant graph neural network, and phonon properties using a highly accurate pretrained ML potential. The ionic dielectric tensor is then computed analytically from these quantities. This approach significantly improves the accuracy of ionic contribution. Using the proposed model, we successfully identified 31 novel high-κ oxides from a screening pool of over 8000 candidates.

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Popular Summary

Finding materials that can store electric energy efficiently is a key for the development of electronics. We used machine learning to discover materials that have both a high dielectric constant and a wide band gap. This combination is useful for making faster, smaller, and more stable devices. Our method first learns how atoms respond to electric fields (Born effective charges) and how they vibrate (phonons) instead of predicting everything at once. By combining these first results with simple physical equations, we accurately calculate how well a material can store energy. We screened more than 8000 oxide materials with this approach and identified 31 that should have excellent dielectric performance. Our physics-guided machine-learning model shows how combining scientific understanding with data opens exciting pathways for discovering advanced materials.

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Phys. Rev. X 16, 021006– Published 7 April, 2026
  • Received 9 October 2025
  • Accepted 25 February 2026
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