Chemistry data from simulations

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.