A physics-and-data co-driven material design strategy for multicomponent diboride ceramics

Abstract

The vast chemical composition space of multicomponent ceramics provides opportunity for regulating their properties and performances. Other than trial-and-error and screening, a physics-and-data co-driven material design strategy was reported to efficiently explore chemical composition space through establishing prediction model between physical properties and chemical composition via physics-guided data mining method. Firstly, causal and correlative relationships were discriminated according to physicochemical knowledge. Secondly, ensemble features such as chemical formula and chemical bond were digitized by constructing descriptors for crystalline solids with atomic features including atomic weight and radius contained in the Periodic Table of elements. Finally, quantitative causal relations between material structure/properties and ensemble descriptors were mined out of small dataset using a least square regression method, on which physical prediction model established therefore for forward predicting structure/properties and inverse designing chemical composition. Choosing diboride ceramics as a case, those causal analytic relations of composition-structure and composition-property were successfully derived and illustrated to predict the value of structure and properties accompanying with quantitative relation between chemical pressure and chemical bonding ensemble descriptors. This method bridges the connection between atomic features and macroscopic properties via ensemble descriptors, which significantly accelerates the discovery of new materials.