The NIST ThermoML archive in particular contains a wealth of experimental measurements for a diverse range of physical properties (Table ). This diversity and range of data, combined with the framework's ability to seamlessly extract, curate, and then estimate those properties, makes the archive a valuable source of data for both training and assessing force fields. More than just offering utilities for importing experimental measurements, the framework offers a full suite of components aimed at making the curation of training and testing data sets as quick and painless as possible. In particular it contains components to filter out unwanted data points, ranging from filtering out data points that were measured outside of a particular temperature, to filtering by the characteristics of the substances the measurement was made for, such as only retaining measurements made for molecules containing alcohol or ester functionalities. Moreover, there are components available to: