This study aims to demonstrate the use of machine learning to generate adsorbate probability distributions for metal-organic frameworks.
Utilized graph neural networks for rapid prediction of adsorbate probability distributions.
Bypassed traditional atomistic simulations to enhance efficiency.
Extracted adsorption binding sites reliably from the predicted distributions.
Successfully generated adsorbate probability distributions quickly using machine learning techniques.
Extracted binding sites from the distributions with high reliability.
Abstract
Adsorbate probability distributions (APDs) of MOFs can be rapidly generated by machine learning, bypassing expensive atomistic simulations. Adsorption binding sites can be reliably extracted from the APDs.