Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., The Journal of Chemical Physics, 150, 2019, 114102.
Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., In: Machine Learning Meets Quantum Physics, Lecture Notes in Physics (Springer), 968, 2020, 277-307.
Replicate our numerical results or reconstruct a force field from your own dataset with a Python implementation of sGDML.
A force field is created via a single command-line call that yields a ready-to-use model file:
$ sgdml all <sgdml_dataset_file> <n_train> <n_validate> [<n_test>]
The last three parameters specify the sizes for the training, validation and test dataset splits, which are sampled from the provided dataset file without overlap. Leave out
<n_test> to use all remaining points for testing (learn more).
A force field model is effectively a parametrization of your dataset that provides energy
e and forces
f for any input geometry
r (learn more):
import numpy as np from sgdml.predict import GDMLPredict from sgdml.utils import io model = np.load('model.npz') gdml = GDMLPredict(model) r,_ = io.read_xyz('geometry.xyz') e,f = gdml.predict(r)
This flexibility enables many applications, e.g. by interfacing to Atomic Simulation Environment (ASE). Here are a few examples:
We offer an experimental model training service for anyone without sufficient compute resources. Simply upload your dataset, schedule some training jobs and return later to collect your model files:
DFT [FHI-aims, light tier 1]
|Benzene (Chmiela et al., 2018)||49,863|
MD17 dataset (Chmiela et al., 2017)
CCSD [Psi4, cc-pVDZ]
CCSD(T) [Psi4, cc-pVDZ]
CCSD(T) [Psi4, cc-pVTZ]