XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr
Heng Liang, Kun Jiang, Tongan Yan, Guang‐Hui Chen
Abstract
mixture, it is found that the prediction performance of XGBoost is also much better than that of the traditional machine learning models although with the unbalanced data. Note that the dimension of features of MOFs is low while the quantity of MOF samples in database is very large, which is suitable for the prediction by model such as XGBoost to search the global minimum of cost function rather than the model involving feature creation. The present study represents the first report using the XGBoost algorithm to discover the MOF adsorbates.
Topics & Concepts
AdsorptionComputer scienceXenonArtificial intelligenceMaterials scienceChemistryPhysical chemistryOrganic chemistryMetal-Organic Frameworks: Synthesis and ApplicationsInorganic Fluorides and Related CompoundsGas Sensing Nanomaterials and Sensors