For on-the-fly active learning of interatomic potentials.
Project description
Carcará
Carcará is a high-performance Python framework designed for atomistic simulations powered by on-the-fly (OTF) machine learning interatomic potentials. It streamlines the integration of first-principles accuracy with the efficiency of classical force fields, enabling the automated development of robust potentials during the simulation process.
Installation
From pip
The easiest way to install Carcará is with pip:
pip install carcara
From github
To install Carcará directly from the GitHub repository, run the following commands:
pip install git+https://github.com/seixas-research/carcara.git
Getting started
License
This is an open source code under MIT License.
Acknowledgements
We thank financial support from INCT Materials Informatics (Grant No. 406447/2022-5), and CNPq (Grant No. 311324/2020-7).
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file carcara-26.4.27.tar.gz.
File metadata
- Download URL: carcara-26.4.27.tar.gz
- Upload date:
- Size: 22.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5ca961c4a1d9dc1dfcd92f83072326756b804588d611a6fb0a5ddc76e1d4b7b3
|
|
| MD5 |
4135b67e4624275b33542911fa376426
|
|
| BLAKE2b-256 |
5659d24c1b8ec9144cc5c0a1ffcd01e7917d26e14afc408c20b5f7355731f9ef
|
File details
Details for the file carcara-26.4.27-py3-none-any.whl.
File metadata
- Download URL: carcara-26.4.27-py3-none-any.whl
- Upload date:
- Size: 21.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7d259f48329447f7437fa43b008e0751e87298c54684d663c2b20e251ff2abfb
|
|
| MD5 |
9f1529558ee4a45b674ed4792b05984d
|
|
| BLAKE2b-256 |
0819302ba240bfb8e940f7a966ab5a36c7fe9b91b520c0f06221a97b2dcee592
|
