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説明:

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 initio accuracy. Here, we demonstrate the utility of ML models in capturing properties of realistic materials by performing finite temperature molecular dynamics simulations of perovskite oxides using a force field based on equivariant graph neural networks. The models demonstrate efficient learning from a small training dataset of energies, forces, stresses, and tensors of Born effective charges. We qualitatively capture the temperature dependence of the dielectric tensor and structural phase transitions in calcium titanate.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Graph neural network, machine learning, dielectrics, perovskite oxides, phase transitions

刊行年月日: 2025-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 2497254

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5451

公開URL: https://doi.org/10.1080/27660400.2025.2497254

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更新時刻: 2025-07-18 10:24:15 +0900

MDRでの公開時刻: 2025-04-24 12:26:37 +0900