Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

We present an approach for quantitatively predicting
the temperature-dependent
single-component adsorption behavior of linear alkanes in silica and
Na-exchanged cationic zeolites using machine learning (ML) models
trained from extensive molecular simulations based on force fields
with coupled cluster accuracy. A high-performing classification model
was developed to distinguish between instances with negligible and
non-negligible adsorption. Subsequently, two ML models were trained
to predict the single-component adsorption loading and the heat of
adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum
ratio. The ML models were trained on International Zeolite Association
(IZA) zeolites, and their transferability to hypothetical zeolites
was successfully validated. We then expand the power of these predictions
to adsorbed mixtures at arbitrary temperatures by integrating them
with the Clausius–Clapeyron equation and ideal adsorbed solution
theory (IAST). This approach was validated and then applied to a temperature
swing adsorption separation process to demonstrate its practical utility.
We demonstrate how predictions from this ML-enabled approach can allow
the selection of high-performing materials that are then validated
using detailed molecular simulations based on quantitatively accurate
force fields.