TY - JOUR
T1 - QUANTUMSHELLNET
T2 - Ground-state eigenvalue prediction of materials using electronic shell structures and fermionic properties via convolutions
AU - Polat, Can
AU - Kurban, Hasan
AU - Kurban, Mustafa
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - Electronic shell structures
KW - Fermionic properties
KW - Ground-state eigenvalue prediction
KW - Quantum mechanical modeling
UR - http://www.scopus.com/inward/record.url?scp=85204029845&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2024.113366
DO - 10.1016/j.commatsci.2024.113366
M3 - Article
AN - SCOPUS:85204029845
SN - 0927-0256
VL - 246
JO - Computational Materials Science
JF - Computational Materials Science
M1 - 113366
ER -