Initial public version of the code

Author: OndrejMarsalek

.gitignore

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+__pycache__/
+.vscode/
+.pytest_cache/
+.pytype/
+.coverage
+htmlcov/
+.ipynb_checkpoints
+*.swp

LICENSE

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+AML
+===
+
+Short Description
+-----------------
+
+This is a Python package to automatically build the reference set
+for the training of _Neural Network Potentials_ (NNPs),
+and eventually other machine-learned potentials,
+in an automated, data-driven fashion.
+For that purpose, a large set of reference configurations
+sampled in a physically meaningful way (typically with molecular dynamics)
+is filtered and the most important points for the representation of the
+_Potential Energy Surface_ (PES) are identified.
+This is done by using a set of NNPs, called a committee, for
+error estimates of individual configurations.
+By iteratively adding the points with the largest
+error in the energy/force prediction, the reference
+set is progressively extended and optimized.
+
+Keywords:
+* Active learning
+* Query by committee
+* Ensemble averaging
+* Committee machines
+* Neural Network Potentials
+
+More information can be found in the following references:
+
+* "Machine learning potentials for complex aqueous systems made simple", C.Schran, F.L.Thiemann, P.Rowe, E.A.Müller, O.Marsalek, A.Michaelides _(submitted)_
+* "Committee neural network potentials control generalization errors and enable active learning", C.Schran, K.Brezina, O.Marsalek, _J. Chem. Phys._, **153**, 104105 (2020), [10.1063/5.0016004](https://doi.org/10.1063/5.0016004)
+
+
+Installation
+------------
+
+For now, just clone the repository and source the `env.sh` file.
+
+Dependencies:
+* NumPy
+* SciPy
+* Matplotlib

README.md

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+# expose the whole public interface
+
+from .io import *
+from .structures import *
+from .mlp import *
+from .qbc import *
+from .utilities import *
+from . import acsf
+from . import constants