A Critical Review of Machine Learning of Energy Materials for POC (Particle in cell codes)
Keywords:
machine learning, particle in cell codeAbstract
Given the numerous challenges, such as “low success probabilities, high time consumption, and high computational cost, inherent in the conventional approaches to developing energy materials, the screening of advanced materials in combination with the modelling of their quantitative structural-activity relationships has recently become one of the hot & trending topics in energy materials.” This necessitates fresh ideas and tools for conducting scientific inquiry in the quest to advance the study of energy materials. Data-driven materials research is thought to change scientific findings and provide new paradigms for the production of energy materials, due to recent advances in artificial intelligence and machine learning. As data-driven materials engineering has advanced, it has become clear that machine learning can be used to significantly enhance these processes, making it possible to create and implement novel energy materials more quickly.
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