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Rapid creation of creative energy materials using machine learning

Rehemon Khan

The development of algorithmic learning (ML) for the identification of increased energy materials has lately been pushed due to its different advantages in artificial intelligence, data analysis, interpolation, and numerical extrapolation, among other fields. To anticipate material qualities, several algorithms have been created. Here, we first describe the structure of each of the ML algorithms employed in material science. Next, we look at the algorithms recently used to functional materials, such as solar cells, batteries, and phase-change materials. Finally, each algorithm’s benefits and drawbacks are examined to help readers chose the best algorithm for an application in question. To assist readers in selecting an appropriate algorithm for certain applications, the benefits and drawbacks of each method are examined.