Dataset Open Access

Data publication: Physics-enhanced neural networks for equation-of-state calculations

Callow, Timothy James


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Cangi, Attila</dc:contributor>
  <dc:contributor>Kraisler, Eli</dc:contributor>
  <dc:contributor>Nikl, Jan</dc:contributor>
  <dc:creator>Callow, Timothy James</dc:creator>
  <dc:date>2023-05-04</dc:date>
  <dc:description>Data for the paper "Physics-enhanced neural networks for equation-of-state calculations"

There are two tar folders:


	
	 trained_models.tar: This contains the final trained models
	
	
	atoMEC_data.tar.gz: This contains the output from the average-atom calculations.
	


For a more detailed explanation of the contents of these folders, please consult the README.md file of this GitHub repository</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/2289</dc:identifier>
  <dc:identifier>10.14278/rodare.2289</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:2289</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-38068</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-37955</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-37075</dc:relation>
  <dc:relation>doi:10.14278/rodare.2288</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Data publication: Physics-enhanced neural networks for equation-of-state calculations</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
138
20
views
downloads
All versions This version
Views 138138
Downloads 2020
Data volume 45.3 GB45.3 GB
Unique views 130130
Unique downloads 1919

Share

Cite as