pcNN_solid

Add-on to install pcNN XC functional into VASP source code.

This add-on is available only to owners of a valid VASP license.

requirements

VASP 5.4.4 with source code (It does not work with VASP 6.)
Python3
PyArmor

Installation

1. Backup "vasp/src/metagga.F" to another directory.
2. Run "patch_implement_pcNN.py". You will be asked the absolute path to metagga.F.
3. "metagga_patched.F" will be generated. Rename it to "metagga.F" and replace "vasp/src/metagga.F" with it.
4. Compile VASP.

Usage

1. Place "nnparams" folder into the same directory as INCAR.

(example)

(target folder) -- nnparams
                -- INCAR
                -- POSCAR
                -- KPOINTS
                -- POTCAR
                ...

2. Add (or modify) keyword "METAGGA = NNMGGA" to INCAR.
3. Run your calculation with the compiled VASP.

Citing this XC functinoal

@misc{nagai2021machinelearningbased, title={Machine-Learning-Based Exchange-Correlation Functional with Physical Asymptotic Constraints}, author={Ryo Nagai and Ryosuke Akashi and Osamu Sugino}, year={2021}, eprint={2111.15593}, archivePrefix={arXiv}, primaryClass={cond-mat.mtrl-sci} }

Notice

This add-on is distributed under an agreement from VASP Software GmbH.

• This add-on comes without any waranty.
• If the add-on is modified by inclusion of additional source code lines from the main VASP source code, the developer must send the revised version of the add-on to VASP Software GmbH for further review.

License

Copyright 2021 Ryo Nagai

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.