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

Abstract

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 were 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:Mn2+, with improved luminance and reliable reproducibility.

Growth and optical spectroscopy of KPb2Cl5 crystal containing Mn2+

“…The electronic absorption, excitation and emission spectra of Mn 2+ have been extensively investigated in a wide variety of hosts [25][26][27][28][29][30][31][32][33][34]. As far as the emission color of Mn 2+ activated phosphors is concerned, it falls into two classes: those with green emission and those with green-to-red emission [35,36].…”

Search for phosphors for use in displays and lighting using heuristics‐based combinatorial materials science

“…It has attracted interest due to its capability of covering a multi-component system, and thereby enhancing the efficiency of exploring new materials for specific purposes. [21][22][23][24][25][26][27] A genetic algorithm (GA) operates on a randomly chosen population by applying Darwin's principle of survival of the fittest. 29 GAs use techniques such as inheritance (elitism), mutation, selection, and crossover.…”

“…[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] We have been working on combinatorial chemistry to search for luminescent materials spanning from a conventional to the heuristic approach, i.e., simple mixing to smart mixing during last few years. [20][21][22][23][24][25][26][27] After the implementation of genetic-algorithm-assisted combinatorial material science (GACMS) we reduced the cost and time by reducing both the size of the population and the number of generations required for satisfactory optimization. [24][25][26][27] However, it is unfortunate that the commercialization of materials through combinatorial chemistry was limited except for a few luminescent materials.…”

“…[20][21][22][23][24][25][26][27] After the implementation of genetic-algorithm-assisted combinatorial material science (GACMS) we reduced the cost and time by reducing both the size of the population and the number of generations required for satisfactory optimization. [24][25][26][27] However, it is unfortunate that the commercialization of materials through combinatorial chemistry was limited except for a few luminescent materials. 20 The causes are many, but one troublesome problem, which may give rise to scale-up complications in the industry, is experimental inconsistency.…”

“…Our interest was not the controllable extrinsic error, but the intrinsic error that cannot be easily controlled because its origin is unknown. 27 In contrast to the readily identifiable causes of extrinsic error, the most problematic and troublesome intrinsic error originates from uncontrollable solution behaviors. Because the precursor solutions used in our experiments contained at least six or more cations, it was nearly impossible to discern either the behavior of each cation or the more complicated interactions among cations in the mixture during the GACMS process.…”