Machine-Learning-Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation.

ABSTRACT: For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, a

SUBMITTER: Xu S 

PROVIDER: S-EPMC8760175 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature