Data for: Prediction of Corrosion Inhibition Efficiency of Pyridines and Quinolines on an Iron Surface using Machine Learning-Powered Quantitative Structure-Property Relationships

Published: 31-03-2020| Version 1 | DOI: 10.17632/rdbkx2y2jv.1
Contributor:
Ming Wah Wong

Description

Calculated values of QM parameters: Dipole Moment, Polarizability, HOMO, LUMO, Ionisation Potential, Electron Affinity, Electronegativity, Hardness, Softness, Electrophilicity, Electron Donor Capacity, Electron Acceptor Capacity, Number of Electrons Transferred, N Atomic Charge, Adsorption Energy (Parallel, Head-On), log P, Van der Waals Surface Area, Solvent Accessible Surface Area and Van der Waals Volume.