Machine learning in the era of smart automation for renewable energy materials

Abstract

Exploration of smart sustainable renewable energy material with the aid of artificial intelligence is gaining momentum as next-generation research. The advantage of high throughput screening of the material is not only increasing the efficiency of the discovery but also can reduce the conventional process. In this review, the machine learning method of investigation of energy material for the application in energy conversion, storage, and energy-efficient materials has been discussed. Various ML tools and closed-loop screening techniques are discussed keeping the emphasis on both material and device optimization. Further, challenges and future prospects of smart automation in the exploration of energy material are elaborated.

Keywords

• Renewable energy
• Solar cell
• Energy efficient material
• Batteries
• Ml tool
• Gaussian regression