Deep learning neural network potential for simulating gaseous adsorption in metal-organic frameworks

Abstract

This study proposes ab initio neural network force fields with physically motivated features to offer superior accuracy in describing adsorbate-adsorbent interactions of nonpolar (CO2) and polar (H2O and CO) molecules in metal-organic frameworks with open-metal sites. Effects of the neural network architecture and features are also investigated for developing accurate models.