报告摘要 | Benefited from recent advances in big-data analytics, the machine learning method was proposed to accelerate discovery of materials with desired properties. In this talk, we apply the data-driven SISSO (Sure Independence Screening and Sparsifying Operator) approach to propose efficient and physically interpretable descriptors to rapidly predict the topological characters and transport coefficients of tetradymites and half-Heusler compounds. Without any input from first-principles calculations, the descriptors contain only several elemental properties of the constituent atoms, and can be readily generalized to systems drastically beyond the training data. Our work also attests to the increasingly important role of such artificial intelligence-based approaches in modern materials discovery.
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