A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies
Xin Li, Bo Li, Ze Yang, Zhiwen Chen, Wang Gao, Qing Jiang
Abstract
We propose a transferable machine-learning model based on the intrinsic descriptors, which can predict the adsorption energies of single-atom alloys, AB intermetallics and high-entropy alloys , simply by training the properties of transition metals.
Topics & Concepts
AdsorptionIntermetallicMaterials scienceAtom (system on chip)Entropy (arrow of time)ThermodynamicsStatistical physicsComputer scienceMetallurgyChemistryPhysical chemistryPhysicsAlloyEmbedded systemMachine Learning in Materials ScienceElectrocatalysts for Energy Conversionnanoparticles nucleation surface interactions