Predicting the Solubility of Gases, Vapors, and Supercritical Fluids in Amorphous Polymers from Electron Density using Convolutional Neural Networks
Oleg Gromov
Abstract:A twin convolutional neural network is proposed to predict the pressure and temperature-dependent sorption of gases, vapors, and supercritical fluids in amorphous polymers based solely on spatial electron density distribution. Quantum chemical data as 3D tensors (3D images) is derived from DFT calculations. A dataset of almost 15000 experimentally measured uptakes (0.01-50 wt%) of 79 gases in 102 different polymers under pressures from 1E-3 – 7E+2 bar range and temperatures from 233-508 K range is collected fr… Show more
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