A neural network LDA functional overfit to water data achieves 1 kcal/mol errors on ionization and atomization energies and matches PBE/B3LYP on WATER27 binding energies after transfer learning from one datum.
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AMTe4 (A=Ta,Nb; M=Ir,Rh) compounds host Weyl points within a few meV of the Fermi energy, including multiple types such as type-I, II, and III in NbRhTe4, substantially revising the topological electronic structure.
Theoretical calculations explain experimental magnetoelastic data in antiferromagnetic MnPt, linking magnetic structure to anisotropy energy and isotropic/anisotropic magnetostriction coefficients.
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Overfitting by design: neural network density functionals for water
A neural network LDA functional overfit to water data achieves 1 kcal/mol errors on ionization and atomization energies and matches PBE/B3LYP on WATER27 binding energies after transfer learning from one datum.
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Fermi energy Weyl nodes in $\mathbf{AM}$Te$_4$ ($\mathbf{A}$=Ta, Nb, $\mathbf{M}$=Ir, Rh)
AMTe4 (A=Ta,Nb; M=Ir,Rh) compounds host Weyl points within a few meV of the Fermi energy, including multiple types such as type-I, II, and III in NbRhTe4, substantially revising the topological electronic structure.
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Magnetoelasticity - magnetic structure interrelation - tetragonal MnPt system study
Theoretical calculations explain experimental magnetoelastic data in antiferromagnetic MnPt, linking magnetic structure to anisotropy energy and isotropic/anisotropic magnetostriction coefficients.