Figure 3 (IMAGE)

Advanced Institute for Materials Research (AIMR), Tohoku University

Figure 3

Caption

Machine learning-assisted design and regression modeling for polymer electrolytes (batteries) and photoresists. (a) Machine learning workflow for polymer electrolyte design. (b) Correlation analysis of molecular descriptors and ionic properties for polymer electrolytes. (c) Regression modeling workflow for predicting the total heat release (THR) of polymeric materials, including data mining, model calibration, K-fold cross-validation, and performance testing. (d) Regression analysis of ionic conductivity versus temperature and the correlation between HOMO/LUMO energy levels and ionic conductivity. (e) Machine learning-driven discovery of novel photoresist materials using DFT data and SHAP feature analysis. (f) Convolutional neural network (CNN)-based classification and optimization of photoresist formulations.

Credit

Hao Li et al.

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