Machine learning and DFT investigation of CO, CO<sub>2</sub>and CH<sub>4</sub>adsorption on pristine and defective two-dimensional magnesene
Siby Thomas, Felix Mayr, Ajith Kulangara Madam, Alessio Gagliardi
Abstract
, resulting in improved sensitivity due to changes in the electronic properties. Additionally, we explored supervised ML regression models to evaluate their ability to act as a surrogate for the DFT-based adsorption energy calculation. Using both system statistics and smooth overlap of atomic position (SOAP)-based featurization, we observed that adsorption energies can be predicted with a mean absolute error of 0.10 eV.
Topics & Concepts
AdsorptionDensity functional theoryMaterials scienceComputational chemistryChemistryPhysical chemistryAdvanced Thermoelectric Materials and DevicesCovalent Organic Framework ApplicationsGas Sensing Nanomaterials and Sensors