Using Autonomous Outlier Detection Methods for Thermophysical Property Data

The reliability and accuracy of thermophysical property data are of central importance for the development of models that describe these properties. In this work, we compare different autonomous algorithms for identifying the outliers in an existing database. Therefore, the comprehensive database on thermophysical property data for the Lennard-Jones fluid [J. Chem. Inf. Model. 2019, 59, 4248-4265] is used. We focus on homogeneous state property data at given temperature and density for the pressure p, thermal expansion coefficient alpha, isothermal compressibility beta, thermal pressure coefficient gamma, internal energy u, isochoric heat capacity c(v), isobaric heat capacity c(p), Gr & uuml;neisen coefficient Gamma, Joule-Thomson coefficient mu(JT), speed of sound w, chemical potential mu, (reduced) Helmholtz energy a = a/T, and its derivatives a(nm). A comprehensive comparison of 19 outlier detection methods is carried out, which provides insights into the applicability of generic outlier detection algorithms for thermophysical property data. Different classes of outlier detection algorithms are included in the study, namely, machine learning, distance-based, density-based, statistical, ensemble, and model-informed. Two approaches are used for the method evaluation: in approach (a), the original database (comprising real outliers) is used. In approach (b), synthetic outliers are introduced. The results and findings from both approaches are consistent. Machine learning methods yield in some cases better performance compared to that of the distance-based, density-based, ensemble, and statistical methods. The best performance is obtained from the model-informed method (called MoDOD). The results also provide insights into the nature of the outliers in the Lennard-Jones database.