Machine learning based energy-free structure predictions of molecules, transition states, and solids

HIGHLIGHTS

• who: Dominik Lemm from the University of Vienna, Vienna, Austria have published the research work: Machine learning based energy-free structure predictions of molecules, transition states, and solids, in the Journal: NATURE COMMUNICATIONS NATURE COMMUNICATIONS
• what: The authors report G2S performance curves for heavy atom coordinates (not hydrogens) of constitutional isomers, carbenes, TS, and elpasolite structure predictions in Fig 2. In Fig 6, the authors compare the resulting performance curves to standard QML machines that had access to the "true" reference coordinates as input, as well as to QML machines that used topology only (input . . .
  

   

  

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