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Title: Machine-Learning-Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation
Authors: Xu, Shidang 
Liu, Xiaoli 
Cai, Pengfei
Li, Jiali 
Wang, Xiaonan 
Liu, Bin 
Keywords: Science & Technology
Physical Sciences
Chemistry, Multidisciplinary
Nanoscience & Nanotechnology
Materials Science, Multidisciplinary
Science & Technology - Other Topics
Materials Science
aggregation-induced emission
machine learning
molecular design
optical properties
solid-state materials
Issue Date: Jan-2022
Publisher: WILEY
Citation: Xu, Shidang, Liu, Xiaoli, Cai, Pengfei, Li, Jiali, Wang, Xiaonan, Liu, Bin (2022-01). Machine-Learning-Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation. ADVANCED SCIENCE 9 (2). ScholarBank@NUS Repository.
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, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials.
ISSN: 2198-3844
DOI: 10.1002/advs.202101074
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