Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies.

ABSTRACT: This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, a

SUBMITTER: Sumita M 

PROVIDER: S-EPMC6161049 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature