Thrilled to share that our latest research, titled ‘QuantumShellNet: Ground-State Eigenvalue Prediction of Materials Using Electronic Shell Structures and Fermionic Properties via Convolutions by Can Polat, Hasan Kurban, and Mustafa Kurban’, has been accepted for publication in Computational Materials Science. This study marks a significant advancement at the intersection of materials science and computer vision, offering new insights into optimizing ab-inito quantum chemistry calculations utulizing convolutions and fermionic properties of elements and molecules.
Kurban Intelligence Lab 