E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

HIGHLIGHTS

• who: Simon Batzner from the Harvard University , Lausanne, Switzerland have published the research work: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, in the Journal: NATURE COMMUNICATIONS NATURE COMMUNICATIONS
• what: The authors show that the proposed method obtains high accuracy compared to existing ML-IP methods across a wide variety of systems, including small_molecules, water in different phases, an amorphous solid, a reaction at a solid/gas interface, and a Lithium superionic conductor. The authors focus on the effects of invariance and equivariance with respect to E(3), i.e . . .
  

   

  

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