Machine Learning for Adsorption Processes

ORAL · Invited

Abstract

Machine Learning for Adsorption Processes - This talk will discuss application of various machine learning approaches that can aid in the discovery of porous materials for gas storage and in the optimization of process conditions for chemical separations.

* This work was primarily supported by the Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under awards DE-FG02-17ER16362 and DE-SC0023454.

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Publication: Y.Z.S. Sun, R.F. DeJaco, and J.I. Siepmann, 'Deep neural network learning of complex binary sorption equilibria from molecular simulation data,' Chem. Sci. 10, 4377–4388 (2019).
K. Shi, Z. Li, D.M. Anstine, D. Tang, C.M. Colina, D.S. Sholl, J.I. Siepmann, and R.Q. Snurr, 'Two-dimensional energy histograms as features for machine learning to predict adsorption in diverse nanoporous materials,' J. Chem. Theory Comput. 23, 4568–4583 (2023).

Presenters

  • J. Ilja Siepmann

    University of Minnesota

Authors

  • J. Ilja Siepmann

    University of Minnesota