Main Article Content
Artificial intelligence-based materials application is one of the hot topics in the field of materials science. Materials are widely used in the space industry, cutting tools, thermal and electrical insulators, and refractory materials. The conventional experiments and statistical approaches usually require more resources and time. Thus, the need for Artificial intelligence applications in the simulation and exploration of novel materials is increasing. Recently, AI has been applied to materials for improving efficiency and prediction accuracy; however, there are many limitations due to the lack of benchmark datasets, advanced pre-processing mechanisms, prediction modelling mechanisms, and simulation tools in the materials literature. Thus it is challenging to identify optimal learning models, including algorithm selection, the architecture of models, data processing, and simulation mechanisms. In this paper, we attempt to review experimental and computational data-based AI mechanisms. Furthermore, the current research status is analyzed for using artificial intelligence techniques in material simulation tools and discovering new materials. Finally, we present research issues of AI-based application realization in materials science.
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