  Large scale Density Functional Theory (DFT) based electronic structure
calculations are highly time consuming and scale poorly with system size. While
semi-empirical approximations to DFT result in a reduction in computational
time versus ab initio DFT, creating such approximations involves significant
manual intervention and is highly inefficient for high-throughput electronic
structure screening calculations. In this letter, we propose the use of
machine-learning for fast and accurate prediction of DFT Hamiltonians. Using
suitable representations of atomic neighborhoods and Kernel Ridge Regression,
we show that an accurate and transferable prediction of DFT Hamiltonians for a
variety of material environments can be achieved. Electronic transmission
spectra computed using predicted Hamiltonians compare accurately with their DFT
counterparts. The method is independent of the specifics of the DFT basis or
material system used and can easily be automated and scaled for predicting
Hamiltonians of any material system of interest.
