Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

HIGHLIGHTS

• who: Shufeng Kong and collaborators from the Cornell University have published the research: Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings, in the Journal: NATURE COMMUNICATIONS NATURE COMMUNICATIONS of 26/01/2022
• what: The authors demonstrate such | https://doi.org/10.1038/s41467-022-28543-x acceleration of materials discovery with a use case based on the identification of band gaps below but near the Fermi energy in metallic systems, which have been shown to be pertinent to thermoelectrics and transparent conductors35,36. Combined with more traditional loss functions, the . . .
  

   

  

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