Gautam Jha

KEN embeds two-dimensional convolutional kernels in an E(3)-equivariant graph neural network so that an interatomic potential reads electronic spin-interaction matrices alongside geometric coordinates, sidestepping the ill-posedness of coordinate-only potentials in strongly correlated magnetic systems. On the HE26 actinide dataset, KENJI reaches a training bonding-energy mean absolute error of 6.93 meV/atom against 44.7 meV/atom for MACE-osaka26, and its training force error of 34.10 meV/Å is on par with 26.30 meV/Å. On the independent test set the energy error is 46.74 meV/atom and the force error exceeds MACE-osaka26, which the authors partly attribute to training without stress.

KEN embeds two-dimensional convolutional kernels in an E(3)-equivariant graph neural network so that an interatomic potential reads electronic spin-interaction matrices alongside geometric coordinates, sidestepping the ill-posedness of coordinate-only potentials in strongly correlated magnetic systems. On the HE26 actinide dataset, KENJI reaches a training bonding-energy mean absolute error of 6.93 meV/atom against 44.7 meV/atom for MACE-osaka26, and its training force error of 34.10 meV/Å is on par with 26.30 meV/Å. On the independent test set the energy error is 46.74 meV/atom and the force error exceeds MACE-osaka26, which the authors partly attribute to training without stress.