AUTHOR=Liu Xiaofeng , Yan Zhiyong , Leng Fangling , Bao Yubin , Huang Yijie 

TITLE=Machine learning predictive model for electronic slurries for smart grids

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1031118

DOI=10.3389/fenrg.2022.1031118

ISSN=2296-598X

ABSTRACT=Electricity is a fundamental energy that is essential to the growth of industrialization and human livelihood. Electric power resources can be used to meet living and production needs more steadily, effectively, and intelligently with the help of an intelligent power grid. The accuracy and stability of component requirements have increased due to the rapid growth of intelligent power networks. One of the fundamental components for component production is electronic slurry, so optimizing electronic paste's properties is crucial for smart grids. In the field of materials science, the process of discovering new materials is drawn out and chance-based. The traditional computation process takes a very long time. Scientists have recently applied machine learning techniques to anticipate material properties and hasten the creation of novel materials. These techniques have proven to offer amazing benefits in a variety of fields. Machine learning techniques, like the cross-validated nuclear ridge regression algorithm to predict double perovskite structure materials and the machine learning algorithm to predict the band gap value of chalcopyrite structure materials, have done a great job of predicting the band gap value of some specific material structures. This targeted prediction model can only predict the band gap value of a single structural material. It can't directly predict how well other structural materials will work. This study shows how to use regression models to divide data sets into different types of elements and into groups. Both of these methods are based on band gap theory, which is the most important theory of physical properties. This plan is more efficient than the classification-regression model. The MAE dropped by 0.0455, the MSE dropped by 0.0425, and the R2 rose by 0.022. The effectiveness of machine learning in forecasting the material band gap value has increased, and the model trained by this design strategy to predict the material band gap value is more reliable than previously.