A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A<sub>2</sub>B<sup>+</sup>B<sup>3+</sup>X<sub>6</sub>
Xiao Wei, Yunong Zhang, Xi Liu, Junjie Peng, Shengzhou Li, Renchao Che, Huiran Zhang
Abstract
Material datasets are high-dimensional and high-noise, which makes most machine learning (ML) methods inefficient. We present a new framework which embeds material domain knowledge into the ML method. By doing so, we illustrate its role and improve the prediction accuracy of 540 perovskite materials.
Topics & Concepts
Perovskite (structure)HalideDomain (mathematical analysis)Noise (video)Domain knowledgeComputer scienceMaterials scienceArtificial intelligenceMachine learningChemistryCrystallographyInorganic chemistryMathematicsMathematical analysisImage (mathematics)Machine Learning in Materials SciencePerovskite Materials and ApplicationsAdvanced Thermoelectric Materials and Devices