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New and improved dielectric materials with high dielectric breakdown strength are required for both high energy density electric energy storage applications and continued miniaturization of electronic devices. Despite much practical significance, accurate ab initio predictions of dielectric breakdown strength for complex materials are beyond the current state-of-the art. Here we take an alternative data-enabled route to address this design problem. Our informatics-based approach employs a transferable machine learning model, trained and validated on a limited amount of accurate data generated through laborious first-principles computations, to predict intrinsic dielectric breakdown strength of several hundreds of chemical compositions in a highly efficient manner. While the adopted approach is quite general, here we take up a specific example of perovskite materials to demonstrate the efficacy of our method …