Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments (doi:10.18419/darus-2136)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

doi:10.18419/darus-2136

DaRUS

2021-10-15

1

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

doi:10.18419/darus-2136

Zaverkin, Viktor (Universität Stuttgart)

Holzmüller, David (Universität Stuttgart)

Steinwart, Ingo (Universität Stuttgart)

Kästner, Johannes (Universität Stuttgart)

EXC 2075 - 390740016

info:eu-repo/grantAgreement/EC/H2020/646717

info:eu-repo/grantAgreement/EC/H2020/646717

DaRUS

Kästner, Johannes

Holzmüller, David

2021-09-15

https://doi.org/10.18419/darus-2136

Chemistry, Computer and Information Science, Physics, GM-NN, Gaussian Moments, Potential Energy Surface, Atomistic Machine Learning, Computational Chemistry

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found <a href="https://gitlab.com/zaverkin_v/gmnn">on GitLab</a>

Basic instructions for installing and running the software can be found in the README.md file.

V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021).

10.1021/acs.jctc.1c00527

V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021).

Label:

.gitignore

application/octet-stream

Label:

.gitlab-ci.yml

application/octet-stream

Label:

LICENSE

text/plain; charset=UTF-8

Label:

pes_training.txt.default

application/octet-stream

Label:

README.md

text/markdown

Label:

requirements.txt

text/plain

Label:

train.py.default

application/octet-stream

Label:

make.bat

application/x-msdownload

Label:

Makefile

text/plain; charset=US-ASCII

Label:

conf.py

text/x-python

Label:

index.rst

application/octet-stream

Label:

install.rst

application/octet-stream

Label:

parameters.rst

application/octet-stream

Label:

adam.rst

application/octet-stream

Label:

calculators.rst

application/octet-stream

Label:

data.rst

application/octet-stream

Label:

layers.rst

application/octet-stream

Label:

neighborlist.rst

application/octet-stream

Label:

pes_training.rst

application/octet-stream

Label:

energy_conservation_tio2.png

image/png

Label:

ethanol.rst

application/octet-stream

Label:

fft_spectrum.png

image/png

Label:

tio2.rst

application/octet-stream

Label:

my_theme.css

text/css

Label:

data_preparation.py

text/x-python

Label:

energy_conservation.py

text/x-python

Label:

ethanol.xyz

chemical/x-xyz

Label:

ethanol_md.py

text/x-python

Label:

ethanol_spectrum.py

text/x-python

Label:

rutile_supercell.xyz

chemical/x-xyz

Label:

tio2_md.py

text/x-python

Label:

adam.py

text/x-python

Label:

calculators.py

text/x-python

Label:

data_pipeline.py

text/x-python

Label:

layers.py

text/x-python

Label:

neighborlist.py

text/x-python

Label:

parameters.py

text/x-python

Label:

pes_fit.py

text/x-python

Label:

trainer.py

text/x-python

Label:

utils.py

text/x-python

Label:

__init__.py

text/x-python

Label:

test_data_pipeline.py

text/x-python

Label:

test_layers.py

text/x-python

Label:

test_neighborlist.py

text/x-python