Machine learning approaches for the prediction of materials properties
Siwar Chibani, François‐Xavier Coudert
Abstract
We give here a brief overview of the use of machine learning (ML) in our field, for chemists and materials scientists with no experience with these techniques. We illustrate the workflow of ML for computational studies of materials, with a specific interest in the prediction of materials properties. We present concisely the fundamental ideas of ML, and for each stage of the workflow, we give examples of the possibilities and questions to be considered in implementing ML-based modeling.
Topics & Concepts
WorkflowField (mathematics)Computer scienceMaterials scienceData scienceMachine learningArtificial intelligenceManagement scienceEngineeringMathematicsDatabasePure mathematicsMachine Learning in Materials ScienceX-ray Diffraction in CrystallographyComputational Drug Discovery Methods