Quantum Chemistry
This is a subset of the original GDB-9-Ex_EOM-CCSD dataset at https://doi.org/10.13139/OLCF/2318313. It consists of 100 randomly selected molecules from the original dataset that consists of 80,593 molecules. This dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Equation of Motion Coupled Cluster (EOM-CCSD) first principles method using the ORCA software.
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This is a subset of the original GDB-9-Ex_TD-DFT-PBE0 dataset at https://doi.org/10.13139/OLCF/2318314. It consists of 100 randomly selected molecules from the original dataset that consists of 96,766 molecules. The dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Time Dependent Density Functional Theory (TDDFT) first principles method using the ORCA software.
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These are the codes and models used in our experiments regarding our submitted article “Cheby-KANs:
Advanced Kolmogorov-Arnold Networks for Applying Geometric Deep Learning in Quantum Chemistry
Applications”. The code is developed using python programming language. In our paper we hae
developed the B-spline based KANs with a more powerful and much faster polynomials “shifted-
Chebyshev polynomials” of the first kind. Also, we integrated our new architecture with geometric deep
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