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In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general-purpose inverse design approach is presented using generative inverse design networks. This ML-based inverse design approach uses backpropagation to calculate the analytical gradients of andoi:10.1002/advs.201902607 pmid:32154072 pmcid:PMC7055566 fatcat:3skflywtcbhg7b37m3sdog25qu