amp-plotconvergence

Atomistic Machine-learning Package (python 3)

Install

All systems
curl cmd.cat/amp-plotconvergence.sh
Debian Debian
apt-get install python3-amp
Ubuntu
apt-get install python3-amp
image/svg+xml Kali Linux
apt-get install python3-amp
Windows (WSL2)
sudo apt-get update sudo apt-get install python3-amp
Dockerfile
dockerfile.run/amp-plotconvergence

python3-amp

Atomistic Machine-learning Package (python 3)

Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the potential energy surface, allowing the user to specify or create descriptor and regression methods. Amp is designed to integrate closely with the Atomic Simulation Environment (ASE). As such, the interface is in pure python, although several compute-heavy parts of the underlying code also have fortran versions to accelerate the calculations. The close integration with ASE means that any calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem, and Gaussian ─ can easily be used as the parent method. This package provides the python 3 modules.