Ab initio machine-learning unveils strong anharmonicity in non-arrhenius self-diffusion of tungsten

HIGHLIGHTS

• What: The authors propose an efficient ab initio framework to compute the Gibbs energy of the transition_state in vacancy-mediated diffusion including the relevant thermal excitations at the density-functional-theory level. The authors show that with the introduced stabilization scheme-implemented in a machine-learning-assisted thermodynamic-integration + directupsampling framework-the full temperature dependence of vacancy migration Gibbs energies can be efficiently calculated, including all relevant thermal excitations with density-functional-theory (DFT) accuracy. To prove the general applicability of TSTI for complex HEAs with different crystalline structures other than BCC W, the authors show preliminary . . .
  

   

  

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