Leveraging machine learning in the innovation of functional materials
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Sun, Zhehao
Yin, Hang
Yin, Zongyou
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The development of novel functional materials holds enormous potential in various fields. This preview highlights the application of theoretical methods and machine learning techniques in a recent issue of Patterns, focusing on the exploration of two-dimensional doped tellurene and the efficient screening of suitable candidates for fin field-effect transistors.
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