The knowledge of viscosity is essential for designing chemical reactors, pipelines, pumps, refrigeration equipment, etc. Several experimental methods exist and are widely used for measuring viscosity; however, the property information demand from industry is much larger than the current rate of experimental data production. Consequently, there is an obvious need for accurate methods that can predict viscosity in a range of pressures and temperatures and be used for validation of existing experimental data. The major challenges of the property are its extremely large variation between different compounds and a very strong dependence on temperature. Group contribution and corresponding state methods traditionally applied for viscosity prediction are not sufficiently accurate for application purposes, which stimulates the search for more effective approaches.
Two groups of thermophysical property prediction methods are under rapid development presently – Quantitative Structure-Property Relationship (QSPR) methods and molecular simulation. Our group, Thermodynamics Research Center at NIST, has experience in applying both methodologies for predicting thermophysical properties of organic compounds (e.g., [1-2]). To support these applications, we maintain one of the largest electronic databases of experimental thermophysical property data in the world (currently, 7 million data points, including viscosity for organic substances in a wide pressure and temperature ranges). We also possess significant computational resources necessary for successful implementation of molecular simulations and machine learning methods. The goal of the opportunity is to develop predictive methods using either of the approaches or a combination of them to reliably predict viscosity of organic compounds of different chemical classes under different external conditions (pressure and temperature).
 W. H. Carande, A. Kazakov, C. Muzny, M. Frenkel. J. Chem. Eng. Data, 2015, 60 (5), 1377–1387.
 R. A. Messerly, M. C. Anderson, S. M. Razavi, R. Elliott. Fluid Phase Equilibria, 2019, 483, 101-115.