MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections

We present a hybrid semiempirical density functional
tight-binding
(DFTB) model with a machine learning neural network potential as a
correction to the repulsive term. This hybrid model, termed machine
learning tight-binding (MLTB), employs the standard self-consistent
charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN
potential as an effective many-body correction for short-range pairwise
repulsive interactions. The MLTB model demonstrates significantly
improved transferability and extensibility compared to the SCC-DFTB
and HIP-NN models. This work provides a practical computational framework
for developing reliable SCC-DFTB models with additional many-body
corrections that more closely approach the DFT level of accuracy.
We illustrate this method with the development of an accurate model
for the thorium–oxygen system, applied to the study of its
nanocluster structures (ThO2)n.