Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network.

ABSTRACT: The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 

SUBMITTER: Nigam A 

PROVIDER: S-EPMC10866360 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature