The QM9 dataset, short for Quantum Mechanics Nine, is a dataset that contains the properties of small organic molecules. These molecules are composed of up to nine heavy atoms taken from the GDB-17 chemical universe. The dataset includes information on energetic, geometric, electronic, and thermodynamic properties of these molecules, making it a valuable resource for researchers in various fields.
Key Features of the QM9 Dataset:
Molecular Structure: The dataset provides detailed information about dataset the 3D molecular structure of each molecule, including bond lengths, angles, and dihedral angles.
Energetic Properties: QM9 includes data on total atomization energy, electronic energy, enthalpy, and free energy of each molecule, allowing researchers to study their energy profiles.
Thermodynamic Properties: Researchers can also access information on heat capacity, entropy, and Gibbs free energy, providing insights into the stability and reactivity of the molecules.
Electronic Properties: The dataset contains data on highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, as well as polarizability and ionization potential.
Applications of the QM9 Dataset
The QM9 dataset has found numerous applications in the fields of chemistry, materials science, and machine learning. Here are some of the key ways in which researchers are leveraging this dataset.