https://github.com/aiqm/ehm-ml
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## ML-based semi-empirical model based-on the extended Hckel method (ML-EHM) This repository contains the EHM-ML data ##### If you use the ML-EHM data or model please cite this paper: ### Machine Learned Hckel Theory: Interfacing Physics and Deep Neural Networks: Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Roman Zubatyuk, Guoqing Zhou, Christopher Koh, Kipton Barros, Olexandr Isayev, Sergei Tretiak. *Machine Learned Hckel Theory: Interfacing Physics and Deep Neural Networks*. arXiv:1909.12963, (https://arxiv.org/abs/1909.12963) ##### More detailed information about COMP6 benchmark(https://github.com/isayev/COMP6), its design and composion can be found in the following publicaiton: Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg. *Less is more: sampling chemical space with active learning*. The Journal of Chemical Physics 148, 241733 (2018), (https://aip.scitation.org/doi/abs/10.1063/1.5023802)
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- Name: AIQM
- Login: aiqm
- Kind: organization
- Repositories: 5
- Profile: https://github.com/aiqm
Open Consortium for AI in Quantum Chemistry
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