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Machine learned approximations to Density Functional Theory Hamiltonians - Towards High-Throughput Screening of Electronic Structure and Transport in Materials

13 Dec 2016 | Online Presentations | Contributor(s): Ganesh Krishna Hegde

We present results from our recent work on direct machine learning of DFT Hamiltonians. We show that approximating DFT Hamiltonians accurately by direct learning is feasible and compare them to existing semi-empirical approaches to the problem. The technique we have proposed requires little...

NEMO5 Tutorial 4B: Device Modeling - Metals

18 Jul 2012 | Online Presentations | Contributor(s): Ganesh Krishna Hegde

Describes some of the modifications made to NEMO5 to include Nth nearest neighbor interactions so that metal electronic structure and transport can be studied. Also includes instructions on how to use NEMO5 input decks to obtain bulk metallic band structures.