Research ArticleMATERIALS SCIENCE

Designing exceptional gas-separation polymer membranes using machine learning

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Science Advances  15 May 2020:
Vol. 6, no. 20, eaaz4301
DOI: 10.1126/sciadv.aaz4301
  • Fig. 1 Robeson plot of selectivity versus permeability for CO2/CH4 separations.

    The 1991 and 2008 Robeson upper bounds are shown as solid black lines. Each data points represents a single polymer; Robeson plots typically contain experimental data for >500 unique polymers. P is in units of Barrer (1 Barrer = 1 ×10−10 cm3[STP] cm2/cm3 s cmHg). Reprinted from (9) with permission.

  • Fig. 2 Assisted design of high-performance polymer membranes.

    The large synthetic toolbox available for creating new polymers is simulated by translating the polymer into a binary “fingerprint,” which is input to the ML algorithm. The model is trained with a random subgroup of polymers from our literature database and then tested against the remaining polymers. The model is then applied to a large set of literature data to discover high-performance polymers, thus facilitating machine-assisted design.

  • Fig. 3 Identification of polymer structures from machine-learning assisted design.

    Results of ML predictions on polymers in the NIMS database for (A) CO2/CH4 and (B) O2/N2 separations. A representative set of the training data is shown in blue for each polymer—note the relative sizes of the data used for training the models compared to that predicted using the ML algorithm. The 1991 and 2008 Robeson upper bounds are shown as dashed and solid lines, respectively.

  • Fig. 4 Polymer candidates for advanced CO2/CH4 gas transport performance identified through ML and their experimental performance.

    CO2/CH4 Robeson plot showing learned permeability/selectivity data. The predicted locations for two potential high-performance polymers are marked with colored crosses—the measured experimental values are denoted with colored dots. The repeat units of both polymers contain functional groups identified through ML as being related to high-performance materials.

  • Table 1 Evaluation of model performance on hold-out test set.

    R2 is the coefficient of determination. Sizes are the number of samples in the training set or test set for each gas. The ML package used was Scikit-learn.

    GasTraining sizeTraining R2Test sizeTest R2
    N25140.9861720.847
    O25230.9851750.903
    H23240.9851090.827
    He2820.980940.799
    CH44200.9901410.904
    CO24710.9861580.875

Supplementary Materials

  • Supplementary Materials

    Designing exceptional gas-separation polymer membranes using machine learning

    J. Wesley Barnett, Connor R. Bilchak, Yiwen Wang, Brian C. Benicewicz, Laura A. Murdock, Tristan Bereau, Sanat K. Kumar

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