Details

Autor(en) / Beteiligte

• Sun, Yangzesheng
• DeJaco, Robert F
• Siepmann, J Ilja

Titel

Deep neural network learning of complex binary sorption equilibria from molecular simulation data

Ist Teil von

• Chemical science (Cambridge), 2019-04, Vol.10 (16), p.4377-4388

Ort / Verlag

England: Royal Society of Chemistry

Erscheinungsjahr

2019

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical ( ) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying process for (1,4-butanediol or 1,5-pentanediol)/water and 1,5-pentanediol/ethanol from all-silica MFI zeolite and 1,5-pentanediol/water from all-silica LTA zeolite. A multi-task deep NN was trained on the simulation data to predict equilibrium loadings as a function of thermodynamic state variables. The NN accurately reproduces simulation results and is able to obtain a continuous isotherm function. Its predictions can be therefore utilized to facilitate optimization of desorption conditions, which requires a laborious iterative search if undertaken by simulation alone. Furthermore, it learns information about the binary sorption equilibria as hidden layer representations. This allows for application of transfer learning with limited data by fine-tuning a pretrained NN for a different alkanediol/solvent/zeolite system.