Exponential growth in publication rates and data generation has yielded tremendous challenges as well as potential rewards for data analysis groups. Data volumes have grown to such a degree that many traditional data collection and interpretation approaches cannot scale sufficiently to remain comprehensive and current, or to effectively track shifting interests within research and industrial communities. It is thus necessary to strongly rely on a substantially increased role for digital archives, automated analysis, and machine learning approaches.<br/><br/>The Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST) maintains an extensive database of published experimental thermophysical and thermochemical properties for pure compounds, binary and ternary mixtures, and chemical reactions. All stored experimental data are associated with estimated combined experimental uncertainties. The large-scale data collection effort is complemented by the Guided Data Capture (GDC) software developed at TRC. GDC is designed to enforce the completeness of the information extracted, validate the information through data definition, range checks, etc., and guide the uncertainty assessment to ensure consistency between compilers with diverse levels of experience The resulting database, in combination with expert system software (ThermoData Engine, TDE), allows on-demand (i.e., dynamic) critical evaluation of thermophysical and thermochemical property data.<br/><br/>While the challenges in implementing such a system are significant, the potential benefits are quite noteworthy. These large, well-vetted data sets generated therewith can be then used as inputs for large scale efforts in chemical modeling, such as chemical candidate screening or development and optimization of property estimation methods. Dynamic access to large validated data sets such as these can also be used to very quickly compare data in submitted manuscripts to a nearly-comprehensive set of existing published data, as well as facilitate robust, property-based literature searches, improving the quality of published information and preventing the propagation of erroneous data. These efforts have facilitated a decade's long collaboration with key journals in the field where reported data are vetted for consistency by TRC before publication. The published data are disseminated in a free and open context via ThermoML, an XML-based file format and IUPAC standard.

Collection, curation, and communication of thermophysical and thermochemical property data at the NIST Thermodynamics Research Center

251st National Meeting (2016)

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