Abstract
The past decade has witnessed extensive applications of artificial intelligence (AI) and robotics in chemistry and material science. However, the current focus mainly revolves around idea execution, neglecting the significance of idea generation, which plays a pivotal role in determining research novelty and potential breakthroughs. Concurrently, the exponential growth of scientific publications has resulted in overpublishing, making it challenging for researchers to grasp multiple fields effectively. As most opportunities for innovation lie in interdisciplinary realms, there is a risk of missing out on the development of new ideas. To address these challenges, we present a deep learning-based AI supervisor trained on correlation-based ScholarNet data of publications. Primarily tailored for material science, this AI supervisor excels in recommending research ideas, analyzing their novelty, and providing comprehensive guidance to researchers. By offering invaluable support in idea generation and novelty assessment, our AI supervisor has emerged as a promising digital infrastructure for future material science research.
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