# CCS_fit - Fitting using Curvature Constrained Splines
[](https://pypi.org/project/ccs-fit/)
[](https://opensource.org/licenses/LGPL-3.0)
[](https://doi.org/10.1016/j.cpc.2020.107602)
[](https://github.com/Teoroo-CMC/CCS/actions)
[](https://teoroo-cmc.github.io/CCS/)
[](https://pypi.org/project/ccs-fit/)
<!--- [](https://github.com/tblite/tblite/actions)
[](https://github.com/Teoroo-CMC/CCS/releases/latest)
[](https://teoroo-cmc.github.io/CCS/)
[](https://codecov.io/gh/tblite/tblite)
[(https://github.com/Teoroo-CMC/CCS/actions)
--->
The `CCS_fit` package is a tool to construct two-body potentials using the idea of curvature constrained splines.
## Getting Started
### Package Layout
```
ccs_fit-x.y.z
├── CHANGELOG.md
├── LICENSE
├── MANIFEST.in
├── README.md
├── bin
│ ├── ccs_build_db
│ ├── ccs_export_sktable
| ├── ccs_export_FF
│ ├── ccs_fetch
│ ├── ccs_fit
│ └── ccs_validate
├── docs
├── examples
│ └── Basic_Tutorial
│ └── tutorial.ipynb
│ └── Advanced_Tutorials
│ ├── CCS
│ ├── CCS_with_LAMMPS
│ ├── DFTB_repulsive_fitting
│ ├── ppmd_interfacing
│ ├── Preparing_ASE_db_trainingsets
│ ├── Search_mode
│ └── Simple_regressor
├── logo.png
├── poetry.lock
├── pyproject.toml
├── src
│ └── ccs
│ ├── ase_calculator
│ ├── common
│ ├── data
│ ├── debugging_tools
│ ├── fitting
│ ├── ppmd_interface
│ ├── regression_tool
│ └── scripts
│ ├── ccs_build_db.py
│ ├── ccs_export_FF.py
│ ├── ccs_export_sktable.py
│ ├── ccs_fetch.py
│ ├── ccs_fit.py
│ └── ccs_validate.py
└── tests
```
* `ccs_build_db` - Routine that builds an ASE-database.
* `ccs_fetch` - Executable to construct the traning-set (structures.json) from a pre-existing ASE-database.
* `ccs_fit` - The primary executable file for the ccs_fit package.
* `ccs_export_sktable` - Export the spline in a dftbplus-compatible layout.
* `ccs_export_FF` - Fit the spline to commonly employed force fields; Buckingham, Morse and Lennard Jones.
* `ccs_validate` - Validation of the energies and forces of the fit compared to the training set.
* `main.py` - A module to parse input files.
* `objective.py` - A module which contains the objective function and solver.
* `spline_functions.py` - A module for spline construction/evaluation/output.
<!---
### Prerequisites
You need to install the following softwares
```
pip install numpy
pip install scipy
pip install ase
pip install cvxopt
```
### Installing from source
#### Git clone
```
git clone git@github.com/Teoroo-CMC/CCS.git
cd CCS
python setup.py install
```
--->
### (Recommended) installing from pip
```
pip install ccs_fit
```
### Installing from source using poetry
```
git clone https://github.com/Teoroo-CMC/CCS_fit.git ccs_fit
cd ccs_fit
# Install python package manager poetry (see https://python-poetry.org/docs/ for more explicit installation instructions)
curl -sSL https://install.python-poetry.org | python3 -
# You might have to add poetry to your PATH
poetry --version # to see if poetry installed correctly
poetry install # to install ccs_fit
```
<!---
### Environment Variables
Set the following environment variables:
```
$export PYTHONPATH=<path-to-CCS-package>:$PYTHONPATH
$export PATH=<path-to-CCS-bin>:$PATH
Within a conda virtual environment, you can update the path by using:
conda develop <path-to-CCS-package>
```
--->
## Tutorials
We provide tutorials in the [examples](examples/) folder. To run the example, go to one of the folders. Each contain the neccesery input files required for the task at hand. A sample `CCS_input.json` for O$_2$ is shown below:
```
{
"General": {
"interface": "CCS"
},
"Train-set": "structures.json",
"Twobody": {
"O-O": {
"Rcut": 2.5,
"Resolution": 0.02,
"Swtype": "sw"
}
},
"Onebody": [
"O"
]
}
```
The `CCS_input.json` file should provide at a minimum the block "General" specifying an interface. The default is to look for input structures in the file `structure.json` file. The format for `structure.json` is shown below :
```
{
"energies":{
"S1": {
"Energy": -4.22425752,
"Atoms": {
"O": 2
},
"O-O": [
0.96
]
},
"S2": {
"Energy": -5.29665634,
"Atoms": {
"O": 2
},
"O-O": [
0.98
]
},
"S3": {
"Energy": -6.20910363,
"Atoms": {
"O": 2
},
"O-O": [
1.0
]
},
"S4": {
"Energy": -6.98075271,
"Atoms": {
"O": 2
},
"O-O": [
1.02
]
}
}
}
```
The `structure.json` file contains different configurations labeled ("S1", "S2"...) and corresponding energy, pairwise distances (contained in an array labelled as "O-O" for oxygen). The stoichiometry of each configuration is given under the atoms label ("Atoms") as a key-value pair ("O" : 2 ).
To perform the fit :
```
ccs_fit
```
The following output files are obtained:
```
CCS_params.json CCS_error.out ccs.log
```
* CCS_params.json - Contains the spline coefficients, and one-body terms for two body potentials.
* error.out - Contains target energies, predicted energies and absolute error for each configuration.
* ccs.log - Contains debug information
## Authors
* **Akshay Krishna AK**
* **Jolla Kullgren**
* **Eddie Wadbro**
* **Peter Broqvist**
* **Thijs Smolders**
## Funding
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957189, and the Swedish National Strategic e-Science programme eSSENCE. The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future.
## License
This project is licensed under the GPLv3 License - see the [LICENSE](LICENSE) file for details.
## Acknowledgement
We want to thank Pavlin Mitev, Christof Köhler, Matthew Wolf, Kersti Hermansson, Bálint Aradi and Tammo van der Heide, and all the members of the [TEOROO-group](http://www.teoroo.kemi.uu.se/) at Uppsala University, Sweden for fruitful discussions and general support.
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"description": "# CCS_fit - Fitting using Curvature Constrained Splines \n\n[](https://pypi.org/project/ccs-fit/)\n[](https://opensource.org/licenses/LGPL-3.0)\n[](https://doi.org/10.1016/j.cpc.2020.107602)\n[](https://github.com/Teoroo-CMC/CCS/actions)\n[](https://teoroo-cmc.github.io/CCS/)\n[](https://pypi.org/project/ccs-fit/)\n\n<!--- [](https://github.com/tblite/tblite/actions)\n[](https://github.com/Teoroo-CMC/CCS/releases/latest)\n[](https://teoroo-cmc.github.io/CCS/)\n[](https://codecov.io/gh/tblite/tblite) \n[(https://github.com/Teoroo-CMC/CCS/actions)\n--->\n\n\n\nThe `CCS_fit` package is a tool to construct two-body potentials using the idea of curvature constrained splines.\n## Getting Started\n### Package Layout\n\n```\nccs_fit-x.y.z\n\u251c\u2500\u2500 CHANGELOG.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 MANIFEST.in\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 bin\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_build_db\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_export_sktable\n| \u251c\u2500\u2500 ccs_export_FF\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_fetch\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_fit\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 ccs_validate\n\u251c\u2500\u2500 docs\n\u251c\u2500\u2500 examples\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Basic_Tutorial\n\u2502\u00a0\u00a0 \u00a0\u00a0 \u2514\u2500\u2500 tutorial.ipynb\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Advanced_Tutorials\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CCS\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 CCS_with_LAMMPS\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 DFTB_repulsive_fitting\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ppmd_interfacing\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Preparing_ASE_db_trainingsets\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 Search_mode\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 Simple_regressor\n\u251c\u2500\u2500 logo.png\n\u251c\u2500\u2500 poetry.lock\n\u251c\u2500\u2500 pyproject.toml\n\u251c\u2500\u2500 src\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 ccs\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ase_calculator\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 common\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 data\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 debugging_tools\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 fitting\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ppmd_interface\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 regression_tool\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 scripts\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_build_db.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_export_FF.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_export_sktable.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_fetch.py\n\u2502\u00a0\u00a0 \u251c\u2500\u2500 ccs_fit.py\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 ccs_validate.py\n\u2514\u2500\u2500 tests\n```\n\n* `ccs_build_db` - Routine that builds an ASE-database.\n* `ccs_fetch` - Executable to construct the traning-set (structures.json) from a pre-existing ASE-database.\n* `ccs_fit` - The primary executable file for the ccs_fit package.\n* `ccs_export_sktable` - Export the spline in a dftbplus-compatible layout.\n* `ccs_export_FF` - Fit the spline to commonly employed force fields; Buckingham, Morse and Lennard Jones.\n* `ccs_validate` - Validation of the energies and forces of the fit compared to the training set.\n* `main.py` - A module to parse input files.\n* `objective.py` - A module which contains the objective function and solver.\n* `spline_functions.py` - A module for spline construction/evaluation/output. \n\n<!---\n### Prerequisites\n\nYou need to install the following softwares\n```\npip install numpy\npip install scipy\npip install ase\npip install cvxopt\n```\n### Installing from source\n\n#### Git clone\n\n```\ngit clone git@github.com/Teoroo-CMC/CCS.git\ncd CCS\npython setup.py install\n```\n--->\n\n### (Recommended) installing from pip\n```\npip install ccs_fit\n```\n\n### Installing from source using poetry\n```\ngit clone https://github.com/Teoroo-CMC/CCS_fit.git ccs_fit\ncd ccs_fit\n\n# Install python package manager poetry (see https://python-poetry.org/docs/ for more explicit installation instructions)\ncurl -sSL https://install.python-poetry.org | python3 -\n# You might have to add poetry to your PATH\npoetry --version # to see if poetry installed correctly\npoetry install # to install ccs_fit\n```\n<!---\n### Environment Variables\nSet the following environment variables:\n```\n$export PYTHONPATH=<path-to-CCS-package>:$PYTHONPATH\n$export PATH=<path-to-CCS-bin>:$PATH\n\nWithin a conda virtual environment, you can update the path by using:\nconda develop <path-to-CCS-package>\n```\n--->\n\n## Tutorials\n\nWe provide tutorials in the [examples](examples/) folder. To run the example, go to one of the folders. Each contain the neccesery input files required for the task at hand. A sample `CCS_input.json` for O$_2$ is shown below:\n```\n{\n \"General\": {\n \"interface\": \"CCS\"\n },\n \"Train-set\": \"structures.json\",\n \"Twobody\": {\n \"O-O\": {\n \"Rcut\": 2.5,\n \"Resolution\": 0.02,\n \"Swtype\": \"sw\"\n }\n },\n \"Onebody\": [\n \"O\"\n ]\n}\n\n```\nThe `CCS_input.json` file should provide at a minimum the block \"General\" specifying an interface. The default is to look for input structures in the file `structure.json` file. The format for `structure.json` is shown below :\n```\n{\n\"energies\":{\n \"S1\": {\n \"Energy\": -4.22425752,\n \"Atoms\": {\n \"O\": 2\n },\n \"O-O\": [\n 0.96\n ]\n },\n \"S2\": {\n \"Energy\": -5.29665634,\n \"Atoms\": {\n \"O\": 2\n },\n \"O-O\": [\n 0.98\n ]\n },\n \"S3\": {\n \"Energy\": -6.20910363,\n \"Atoms\": {\n \"O\": 2\n },\n \"O-O\": [\n 1.0\n ]\n },\n \"S4\": {\n \"Energy\": -6.98075271,\n \"Atoms\": {\n \"O\": 2\n },\n \"O-O\": [\n 1.02\n ]\n }\n}\n}\n```\nThe `structure.json` file contains different configurations labeled (\"S1\", \"S2\"...) and corresponding energy, pairwise distances (contained in an array labelled as \"O-O\" for oxygen). The stoichiometry of each configuration is given under the atoms label (\"Atoms\") as a key-value pair (\"O\" : 2 ). \n\n\nTo perform the fit : \n```\nccs_fit\n```\nThe following output files are obtained:\n```\nCCS_params.json CCS_error.out ccs.log \n```\n* CCS_params.json - Contains the spline coefficients, and one-body terms for two body potentials.\n* error.out - Contains target energies, predicted energies and absolute error for each configuration.\n* ccs.log - Contains debug information\n## Authors\n\n* **Akshay Krishna AK** \n* **Jolla Kullgren** \n* **Eddie Wadbro** \n* **Peter Broqvist**\n* **Thijs Smolders**\n\n## Funding\nThis project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957189, and the Swedish National Strategic e-Science programme eSSENCE. The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future.\n\n## License\nThis project is licensed under the GPLv3 License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgement\nWe want to thank Pavlin Mitev, Christof K\u00f6hler, Matthew Wolf, Kersti Hermansson, B\u00e1lint Aradi and Tammo van der Heide, and all the members of the [TEOROO-group](http://www.teoroo.kemi.uu.se/) at Uppsala University, Sweden for fruitful discussions and general support.\n\n",
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