Zeolite Adsorption Property Prediction using Deep Learning

arXiv:2403.12659v1 Announce Type: cross
Abstract: 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 …

abstract arxiv benefit cond-mat.mtrl-sci cs.lg deep learning design materials novel prediction process property simulation space type work