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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Tsz Wai Ko1*, Jonas A. Finkler2*, Stefan Goedecker2*, Jörg Behler1*

1 Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstraße 6, 37077, Göttingen, Germany

2 Department of Physics, Universität Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland

* Corresponding authors emails: tko@chemie.uni-goettingen.de, jonas.finkler@unibas.ch, Stefan.Goedecker@unibas.ch, joerg.behler@uni-goettingen.de
DOI10.24435/materialscloud:f3-yh [version v1]

Publication date: Nov 04, 2020

How to cite this record

Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler, A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer, Materials Cloud Archive 2020.137 (2020), doi: 10.24435/materialscloud:f3-yh.

Description

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations.

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Files

File name Size Description
readme.txt
MD5md5:8a7eb0061688d4014848bc37009210f1
2.5 KiB The description of the datasets
datasets.tar.gz
MD5md5:0aa11ea305b3af6a47bb3c577c2729bc
37.9 MiB Datasets used for constructing 4G-HDNNPs

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Paper in which the data is used)

Keywords

Machine learning potentials Non-local charge transfer DFT SNSF

Version history:

2020.137 (version v1) [This version] Nov 04, 2020 DOI10.24435/materialscloud:f3-yh