Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method.

When using methods to obtain high-quality quantum behavior of molecules, it often involves a lot of trial-and-error work in algorithm design and parameter selection, which requires enormous time and computational resource costs. In the study of vibrational energies of diatomic molecules, we found that starting from a low-precision DFT model and then correcting the errors using the high-dimensional function modeling capabilities of machine learning, one can considerably reduce the computational burden and improve the prediction accuracy. Data-driven machine learning is able to capture subtle physical information that is missing from DFT approaches.

Investigations on the local structure and the spin Hamiltonian parameters for Cu in xCuO-(68-x)V O -32TeO glasses.

The defect structure, spin Hamiltonian parameters (SHPs: anisotropic factors and and the hyperfine structure constants and ), and their compositional dependence of in ( = 5, 10, 20, 30 mol%) glasses are quantitatively analyzed by using the higher-order perturbation formula of octahedral complex with tetrahedral elongation distortion. Due to the Jahn-Teller effect, the group is subjected to tetragonal elongation distortion of varying degrees. , and show nonlinear changes with the concentrations of .