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Issue 16, 2019
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Deep neural network learning of complex binary sorption equilibria from molecular simulation data

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Abstract

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical (N1N2VT) 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.

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

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Publication details

The article was received on 30 Nov 2018, accepted on 17 Mar 2019 and first published on 18 Mar 2019


Article type: Edge Article
DOI: 10.1039/C8SC05340E
Chem. Sci., 2019,10, 4377-4388
  • Open access: Creative Commons BY license
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    Deep neural network learning of complex binary sorption equilibria from molecular simulation data

    Y. Sun, R. F. DeJaco and J. I. Siepmann, Chem. Sci., 2019, 10, 4377
    DOI: 10.1039/C8SC05340E

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