A New Paradigm for Materials Discovery: Heuristics-Assisted Combinatorial Chemistry Involving Parameterization of Material Novelty

The combinatorial chemistry (combi‐chem) of inorganic functional materials has not yet led to the discovery of commercially interesting materials, in contrast to the many successful discoveries of heterogeneous catalysts leading to commercialization. Novel materials for practical use are likely hidden in the multicompositional search space that contains an infinite number of possible stoichiometries, as well as a large number of well‐known materials. To discover new, inorganic luminescent materials (phosphors) from the SrO‐CaO‐BaO‐La2O3‐Y2O3‐Si3N4‐Eu2O3 search space, heuristics optimization strategies, such as the non‐dominated‐sorting genetic algorithm (NSGA) and particle swarm optimization (PSO) are coupled with high‐throughput experimentation (HTE) in such a manner that the experimental evaluation of fitness functions for the NSGA and PSO is accomplished by the HTE. The proposed strategy also involves the parameterization of the material novelty to avoid systematically a futile convergence on well‐known, already‐established materials. Although the process starts with random compositions, we finally converge on a novel, single‐phase, yellow‐green‐emitting luminescent material, La4–xCaxSi12O3+xN18−x:Eu2+, that has strong potential for practical use in white light‐emitting diodes (WLEDs). Heuristics optimization‐assisted combinatorial chemistry involving parameterization of material novelty makes it possible to discover a single‐phase, yellow‐green‐emitting luminescent material, La4–xCaxSi12O3+xN18−x:Eu2+, which has a strong potential for practical use in white‐light‐emitting diodes (WLEDs). The heuristics optimization involves a non‐dominated‐sorting genetic algorithm and particle‐swarm optimization, and is applied together with a parameterization of the material novelty.