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

Polat C., Kurban H., KURBAN M.

COMPUTATIONAL MATERIALS SCIENCE, vol.246, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 246
  • Publication Date: 2025
  • Doi Number: 10.1016/j.commatsci.2024.113366
  • Journal Name: COMPUTATIONAL MATERIALS SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Ankara University Affiliated: Yes

Abstract

Efficient and precise characterization of material properties is critical in quantum mechanical modeling. While Density Functional Theory (DFT) 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. .