A repository containing code to run data-driven coupled-cluster singles and doubles (DDCCSD), as defined in references 1 and 2. For ease of use, install the conda environment following the instructions in the repository DDQC_Demo. In that same repository, an example using the method from reference 1 can be found. In this repository, we have included a simple example for the method in reference 2: hydrocarbon_Pair_energy.ipynb.
helper_CC_ML_spacial.pyhelper_ML_tools.pyhelper_ML_pairtools.py
@article{townsend2019data,
title={Data-driven acceleration of the coupled-cluster singles and doubles iterative solver},
author={Townsend, Jacob and Vogiatzis, Konstantinos D},
journal={The journal of physical chemistry letters},
volume={10},
number={14},
pages={4129--4135},
year={2019},
publisher={ACS Publications}
}
@article{townsend2020transferable,
title={Transferable MP2-based machine learning for accurate coupled-cluster energies},
author={Townsend, Jacob and Vogiatzis, Konstantinos D},
journal={Journal of Chemical Theory and Computation},
volume={16},
number={12},
pages={7453--7461},
year={2020},
publisher={ACS Publications}
}