sgdml 1.0.2

Description:

sgdml 1.0.2

Symmetric Gradient Domain Machine Learning (sGDML)
For more details visit: sgdml.org
Documentation can be found here: docs.sgdml.org
Requirements:

Python 3.7+
PyTorch (>=1.8)
NumPy (>=1.19)
SciPy (>=1.1)

Optional:

ASE (>=3.16.2) (to run atomistic simulations)

Getting started
Stable release
Most systems come with the default package manager for Python pip already preinstalled. Install sgdml by simply calling:
$ pip install sgdml

The sgdml command-line interface and the corresponding Python API can now be used from anywhere on the system.
Development version
(1) Clone the repository
$ git clone https://github.com/stefanch/sGDML.git
$ cd sGDML

...or update your existing local copy with
$ git pull origin master

(2) Install
$ pip install -e .

Using the flag --user, you can tell pip to install the package to the current users's home directory, instead of system-wide. This option might require you to update your system's PATH variable accordingly.
Optional dependencies
Some functionality of this package relies on third-party libraries that are not installed by default. These optional dependencies (or "package extras") are specified during installation using the "square bracket syntax":
$ pip install sgdml[<optional1>]

Atomic Simulation Environment (ASE)
If you are interested in interfacing with ASE to perform atomistic simulations (see here for examples), use the ase keyword:
$ pip install sgdml[ase]

Reconstruct your first force field
Download one of the example datasets:
$ sgdml-get dataset ethanol_dft

Train a force field model:
$ sgdml all ethanol_dft.npz 200 1000 5000

Query a force field
import numpy as np
from sgdml.predict import GDMLPredict
from sgdml.utils import io

r,_ = io.read_xyz('geometries/ethanol.xyz') # 9 atoms
print(r.shape) # (1,27)

model = np.load('models/ethanol.npz')
gdml = GDMLPredict(model)
e,f = gdml.predict(r)
print(e.shape) # (1,)
print(f.shape) # (1,27)

Authors

Stefan Chmiela
Jan Hermann

We appreciate and welcome contributions and would like to thank the following people for participating in this project:

Huziel Sauceda
Igor Poltavsky
Luis Gálvez
Danny Panknin
Grégory Fonseca
Anton Charkin-Gorbulin

References


[1] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R.,
Machine Learning of Accurate Energy-conserving Molecular Force Fields.
Science Advances, 3(5), e1603015 (2017)
10.1126/sciadv.1603015


[2] Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A.,
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields.
Nature Communications, 9(1), 3887 (2018)
10.1038/s41467-018-06169-2


[3] Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A.,
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning.
Computer Physics Communications, 240, 38-45 (2019)
10.1016/j.cpc.2019.02.007


[4] Chmiela, S., Vassilev-Galindo, V., Unke, O. T., Kabylda, A., Sauceda, H. E., Tkatchenko, A., Müller, K.-R.,
Accurate Global Machine Learning Force Fields for Molecules With Hundreds of Atoms.
Science Advances, 9(2), e1603015 (2023)
10.1126/sciadv.adf0873