Because of this lack of accessible experimental data , machine learning predictions in chemistry are generally too error-prone to realize the potential of the new methods at this time. This necessitates a change in the way chemists publish their data and the type of data published . The call for open data, open source, and open science (ODOSOS) in chemistry is not new , but with the advent of more powerful data-driven algorithms, it has never been more important.