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

Yangzesheng Sun, Robert F. Dejaco, J. Ilja Siepmann

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4377-4388
Number of pages12
JournalChemical Science
Volume10
Issue number16
DOIs
StatePublished - 2019

Bibliographical note

Funding Information:
This research is primarily supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (for machine learning and molecular simulations of MFI-C4-W and MFI-C5-E) and also by the DOE Office of Energy Efficiency and Renewable Energy under Award No. DEEE0006878 (for molecular simulations of MFI/LTA-C5-W). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Additional computer resources were provided by the Minnesota Supercomputing Institute at the University of Minnesota.

Publisher Copyright:
© 2019 The Royal Society of Chemistry.

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