Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten":<br>
<ul>
<li>DFT input files: INCAR, KPOINTS.</li>
<li>All POSCAR files for DFT and thermodynamic integration</li>
<li>Moment tensor potential (MTP) file</li>
<li>Training dataset for MTP</li>
<li>All Hessian Matrix files for thermodynamic integration. For the transition state, the stabilized Hessian Matrix is provided.</li>
<li>All imaginary mode files for transition state</li>
<li>Lattice expansion used in all calculations.</li>
</ul>