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Journal of Materials Informatics
Overwhelming evidence has been accumulating that materials informatics can provide a novel solution for materials discovery. While the conventional approach to innovation relies mainly on experimentation, the generative models stemming from the field of machine learning can realize the long-held dream of inverse design, where properties are mapped to the chemical structures. In this review, we introduce the general aspects of inverse materials design and provide a brief overview of twodoi:10.20517/jmi.2021.07 fatcat:i2rvycr73zeznii43zltreiloy