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

Can Polat, HASAN KURBAN, Mustafa Kurban

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.