QUANTUMSHELLNET: Ground-state eigenvalue prediction of materials using electronic shell structures and fermionic properties via convolutions

Can Polat, Hasan Kurban, Mustafa Kurban*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Efficient and precise characterization of material properties is critical in quantum mechanical modeling. While Density Functional Theory (DFT) remains a foundational method for analyzing material properties, it faces scalability challenges and precision limitations, especially with complex materials. This study introduces QUANTUMSHELLNET, a novel vision-based approach that combines an orbital encoder and a physics-informed deep neural network. QUANTUMSHELLNET is specifically designed to rapidly and accurately predict ground-state eigenvalues in materials by leveraging electronic shell structures and their fermionic properties. Experiments conducted across a diverse range of elements and molecules show that QUANTUMSHELLNET outperforms traditional DFT as well as modern machine learning methods, including PSIFORMER and FERMINET.

Original languageEnglish
Article number113366
JournalComputational Materials Science
Volume246
DOIs
Publication statusPublished - Jan 2025

Keywords

  • Deep neural networks
  • Electronic shell structures
  • Fermionic properties
  • Ground-state eigenvalue prediction
  • Quantum mechanical modeling

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