XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

• Published in: ACS omega Vol. 6; no. 13; pp. 9066 - 9076
• Main Authors: Liang, Heng, Jiang, Kun, Yan, Tong-An, Chen, Guang-Hui
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 06.04.2021

The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal–organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was p...