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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 manual intervention or arbitrary model parameters and can be applied to any material system or geometry for quick-yet-accurate predictions of DFT Hamiltonians.
NEMO5 Tutorial 4B: Device Modeling - Metals
18 Jul 2012 | Online Presentations | Contributor(s): Ganesh Krishna Hegde