Discovery of New Green Phosphors and Minimization of Experimental Inconsistency Using a Multi-Objective Genetic Algorithm-Assisted Combinatorial Method

A multi-objective genetic algorithm-assisted combinatorial materials search (MOGACMS) strategy was employed to develop a new green phosphor for use in a cold cathode fluorescent lamp (CCFL) for a back light unit (BLU) in liquid crystal display (LCD) applications. MOGACMS is a method for the systematic control of experimental inconsistency, which is one of the most troublesome and difficult problems in high-throughput combinatorial experiments. Experimental inconsistency is a very serious problem faced by all scientists in the field of combinatorial materials science. For this study, experimental inconsistency and material property was selected as dual objective functions that were simultaneously optimized. Specifically, in an attempt to search for promising phosphors with high reproducibility, luminance was maximized and experimental inconsistency was minimized using the MOGACMS strategy. A divalent manganese-doped alkali alkaline germanium oxide system was screened using MOGACMS. As a result of MOGA reiteration, we identified a phosphor, Na2MgGeO4:Mn-2+,Mn- with improved luminance and reliable reproducibility.