Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules.

ABSTRACT: Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our

SUBMITTER: Li X 

PROVIDER: S-EPMC8372320 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature