Extending Shannon's ionic radii database using machine learning
Ahmer A. B. Baloch, Saad M. Alqahtani, Faisal Mumtaz, Ali H. Muqaibel, Sergey N. Rashkeev, Fahhad H. Alharbi
Abstract
The authors extend the ionic radii database of Shannon's seminal work using machine learning regression. The developed consolidated table will allow prediction of material properties with high accuracy by considering the definite ionic radius value based on the oxidation state and coordination number. The work is relevant to the evolving material informatics field and has applications in many related fields.
Topics & Concepts
Materials scienceIonic radiusField (mathematics)RADIUSIonic bondingMaterials informaticsTable (database)Work (physics)InformaticsMachine learningComputer scienceArtificial intelligenceDatabaseValue (mathematics)State (computer science)Data miningComputational scienceIonic liquidEngineering drawingMachine Learning in Materials ScienceInorganic Chemistry and MaterialsX-ray Diffraction in Crystallography