Materials representation and transfer learning for multi-property prediction

HIGHLIGHTS

  • who: Shufeng Kong and collaborators from the Department of Computer Science, Cornell University, Ithaca, New York, USA have published the article: Materials representation and transfer learning for multi-property prediction, in the Journal: (JOURNAL)
  • what: The work aims to emulate a scientist's aggregation of multiple knowledge sources to make informed predictions in new spaces. The recent proliferation of deep learning for materials property prediction demonstrates the rapid advancement of AI for materials discovery and this work makes a leap forward through seamless integration of machine_learning techniques in a framework specifically designed to address the . . .

     

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