COMPUTATIONAL MATERIALS SCIENCE, cilt.246, 2025 (SCI-Expanded)
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. .