Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

Tailoring
the structure and chemistry of metal–organic frameworks
(MOFs) enables the manipulation of their adsorption properties to
suit specific energy and environmental applications. As there are
millions of possible MOFs (with tens of thousands already synthesized),
molecular simulation has frequently been used to rapidly evaluate
the adsorption performance of a large set of MOFs. This allows subsequent
experiments to focus only on a small subset of the most promising
MOFs. In many instances, however, even molecular simulation becomes
prohibitively time-consuming, underscoring the need for alternative
screening methods, such as machine learning, to precede molecular
simulation efforts. In this study, as a proof of concept, we trained
a neural networkspecifically, a multilayer perceptron (MLP)as
the first example of a machine learning model capable of predicting
full adsorption isotherms of different molecules not included in the
training of the model. To achieve this, we trained our MLP on “alchemical”
species, represented only by variables derived from their force-field
parameters, to predict the loadings of real adsorbates. Alchemical
species used for training were small, near-spherical, and nonpolar,
enabling the prediction of analogous real molecules relevant for chemical
separations such as argon, krypton, xenon, methane, ethane, and nitrogen.
MOFs were also represented by simple descriptors (e.g., geometric
properties and chemical moieties). The trained model was shown to
make accurate adsorption predictions for these six adsorbates in both
hypothetical and existing MOFs. The MLP presented here is not expected
to be applied “as is” to more complex adsorbates with
properties not considered during its training. However, our results
illustrate a new philosophy of training that can be built upon with
the goal of predicting adsorption isotherms in not only a database
of MOFs but also a database of adsorbates and over a range of relevant
operating conditions.