N-type polymer thermoelectric performance prediction method based on structural decoupling and neural network

Abstract

The application discloses a kind of N-type high molecular thermoelectric performance prediction method based on structure decoupling and neural network, the method comprises the following steps: S1, data preprocessing: the molecular formula of N-type high molecular polymer is handled into SMILES sequence;S2, structure decoupling: the connection between the skeleton and side chain is cut off to SMILES sequence and is identified;S3, skeleton feature extraction, skeleton global graph feature vector is extracted by GINE graph neural network;S4, side chain feature extraction, side chain feature vector is generated by ANN artificial neural network;S4, isomeric fusion and prediction, the fusion feature vector is input into fusion sensor, and the output N-type high molecular thermoelectric performance prediction value is obtained by fusion sensor.The application realizes the prediction of N-type high molecular thermoelectric performance effectively by the deep learning architecture of skeleton-side chain decoupling, and greatly reduces the expensive experimental cost and risk required by traditional research and development mode such as "blind trial and error".

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