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Abstract The “ovonic threshold switching (OTS)” selector is the key component in modern semiconductor technology for 3D memory integration. The insufficient understanding of OTS physics, however, has impeded the materials innovation and industrial advancement. In the cover, the authors illustrate a machine learning strategy to shed light on the OTS mechanism. A vast amount of structural features are collected to train the neural network so that it can recognize the defect states which are known as the origin of OTS behavior in chalcogenide glass. The machine learning method allows researchers to design disordered functional materials from first principles. (DOI: 10.1002/inf2.12315) image