(763b) A COSMO-Based Inverse Machine Learning Application for Mixture Product Design
Inspired by the well-known optimization-based Computer-Aided Molecular Design (CAMD) approach , an inverse application framework of machine learning for mixture product design is proposed in this study. First, a ML model is established to verify the correlation between structural descriptors of the product and its target properties. Specifically, the Artificial Neural Network is utilized to formulate the ML model, and Ï-profile spectra descriptors generated from COnductor-like Screening MOdel (COSMO)  are employed as the structural descriptors. Such descriptors of a product are represented by ten SÏ-profile descriptors, which are the integrations of ten areas from one Ï-profile spectrum. Next, the Inverse Neural Network is established where the inputs are target properties while the outputs are structural descriptors. Notably, since the number of inputs for the design phase is often less than the outputs, multiple ML models are established, and each model is responsible for predicting one SÏ-profile descriptor. Afterwards, all the established ML models are employed to design the ten SÏ-profile descriptors for potential products based on the target properties. Since the COSMO spectrum of a mixture is equal to the linear combination of COSMO spectra of its ingredients, potential combination of ingredients is screened out within an assigned database  and further selected through a statistical tool based on Euclidean distance. Finally, this framework is demonstrated using a fragrance design case study, and the results are verified by using experimental data from the literature .
The authors gratefully acknowledge the financial support from âNatural Science Foundation of Chinaâ (No. 21808025) and âthe Fundamental Research Funds for the Central Universities DUT17RC(3)008â.
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