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The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

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Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec (2018)Highlighted by Jan JensenEver since Aln Aspuru-Guzik and co-workers published their seminal paper there has been a flurry of activity on generative models, which is not surprising...