Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

Description

This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials" It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.
Show more

Year of publication

2022

Authors

Department of Electrical Engineering and Automation

Bora Karasulu - Creator

George A. Marchant - Creator

Livia B. Partay - Creator

Miguel A. Caro Orcid -palvelun logo - Creator

University of Warwick - Contributor

Zenodo - Publisher

Other information

Fields of science

Computer and information sciences

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials - Research.fi