The ability to efficiently predict adsorption properties of zeolites can be of large benefit in accelerating the design process of novel materials. The existing configuration space for these materials is wide, while existing molecular simulation methods are computationally expensive. In this work, we propose a model which is 4 to 5 orders of magnitude faster at adsorption properties compared to molecular simulations. To validate the model, we generated datasets containing various aluminium configurations for the MOR, MFI, RHO and ITW zeolites along with their heat of adsorptions and Henry coefficients for CO$_2$, obtained from Monte Carlo simulations. The predictions obtained from the Machine Learning model are in agreement with the values obtained from the Monte Carlo simulations, confirming that the model can be used for property prediction. Furthermore, we show that the model can be used for identifying adsorption sites. Finally, we evaluate the capability of our model for generating novel zeolite configurations by using it in combination with a genetic algorithm.

翻译：高效预测沸石吸附性质的能力可极大加速新型材料的设计进程。这类材料的现有构型空间非常广阔，而现有分子模拟方法计算成本高昂。在本研究中，我们提出一种模型，其吸附性质预测速度比分子模拟快4至5个数量级。为验证该模型，我们生成了包含MOR、MFI、RHO和ITW沸石中不同铝构型及其CO$_2$吸附热与亨利系数的数据集，这些数据均通过蒙特卡洛模拟获得。机器学习模型的预测结果与蒙特卡洛模拟值吻合，证实该模型可用于性质预测。此外，我们展示了该模型在识别吸附位点方面的应用。最后，通过将模型与遗传算法结合，评估了其生成新型沸石构型的能力。