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ccs-fit-0.21.3
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مشاهده بیشترتوضیحات
Fitting tools for repulsive two body interactions using curvature constrained splines.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ccs-fit-0.21.3 |
| نام | ccs-fit |
| نسخه کتابخانه | 0.21.3 |
| نگهدارنده | ['Jolla Kullgren'] |
| ایمیل نگهدارنده | ['jolla.kullgren@kemi.uu.se'] |
| نویسنده | Akshay Krishna AK |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/Teoroo-CMC/CCS |
| آدرس اینترنتی | https://pypi.org/project/ccs-fit/ |
| مجوز | GPL-3 |
# 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 O2 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.
## 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.
نیازمندی
| مقدار | نام |
|---|---|
| >=3.22.1 | ase |
| >=1.3.0 | cvxopt |
| >=1.22.4,<1.23.0 | numpy |
| >=1.5.0 | pandas |
| >=6.0 | pyyaml |
| >=1.2.1,<2.0.0 | scikit-learn |
| >=1.9.2 | scipy |
| >=1.11.1 | sympy |
| >=4.64.1 | tqdm |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl ccs-fit-0.21.3:
pip install ccs-fit-0.21.3.whl
نصب پکیج tar.gz ccs-fit-0.21.3:
pip install ccs-fit-0.21.3.tar.gz