Issue 13, 2022

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.

Graphical abstract: Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks

Supplementary files

Article information

Article type
Communication
Submitted
06 Dec. 2021
Accepted
26 Maijs 2022
First published
28 Maijs 2022
This article is Open Access
Creative Commons BY-NC license

Mater. Adv., 2022,3, 5299-5303

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

C. Yang, I. Pandey, D. Trinh, C. Chen, J. D. Howe and L. Lin, Mater. Adv., 2022, 3, 5299 DOI: 10.1039/D1MA01152A

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