Chemistry data from simulations
- Submission status
- Open
- Submission deadline
Chemical data is a vital feedstock for research to support global challenges; however, the velocity and volume of experimental data sharing is still limited in comparison to other fields. Theoretical chemical data provides a viable resource to bridge that gap. This Collection presents a series of articles describing annotated datasets of theoretical chemistry datasets. Data are presented without hypotheses or significant analyses, to support improvements in chemical synthesis, machine learning, materials design, drug discovery, and improvements to the theory and practice of in silico chemistry.
This Collection welcomes submissions of Data Descriptors (full information on content types can be found here). Papers will be published in Scientific Data as soon as they are accepted and then collected together and promoted on the Collection homepage. Collections are associated with a call for papers and are managed by the journal's Editors.
This Collection welcomes submissions from all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Data more generally. See our editorial process page for more details.
All submissions are subject to the same peer review process and editorial standards as regular Scientific Data articles, including the journal’s policy on competing interests. See our Collections guidelines for more details.
This Collection is not supported by sponsorship.
