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.