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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

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

importnumpyasnpfromsgdml.predictimportGDMLPredictfromsgdml.utilsimportior,_=io.read_xyz('geometries/ethanol.xyz') # 9 atomsprint(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

Name		Name	Last commit message	Last commit date
scripts		scripts
sgdml		sgdml
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

Name		Name	Last commit message	Last commit date
scripts		scripts
sgdml		sgdml
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

Name		Name	Last commit message	Last commit date
scripts		scripts
sgdml		sgdml
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

Name		Name	Last commit message	Last commit date
scripts		scripts
sgdml		sgdml
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

Name		Name	Last commit message	Last commit date
scripts		scripts
sgdml		sgdml
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

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Symmetric Gradient Domain Machine Learning (sGDML)

Requirements:

Optional:

Getting started

Stable release

Development version

(1) Clone the repository

(2) Install

Optional dependencies

Atomic Simulation Environment (ASE)

Reconstruct your first force field

Query a force field

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Symmetric Gradient Domain Machine Learning (sGDML)

Requirements:

Optional:

Getting started

Stable release

Development version

(1) Clone the repository

(2) Install

Optional dependencies

Atomic Simulation Environment (ASE)

Reconstruct your first force field

Query a force field

Authors

References

About

Topics

Resources

Stars

Watchers

Forks

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Packages

Used by

Contributors

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Latest commit

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Repository files navigation

Symmetric Gradient Domain Machine Learning (sGDML)

Requirements:

Optional:

Getting started

Stable release

Development version

(1) Clone the repository

(2) Install

Optional dependencies

Atomic Simulation Environment (ASE)

Reconstruct your first force field

Query a force field

Authors

References

About

Topics

Resources

Stars