Problem: Garbage in, garbage out
Computer simulations are playing an increasingly important role in process and product development but rely fully on the accuracy of the physical property data used. As experimental data is often unavailable or unreliable, accurately predicting physical properties of components and mixtures is typically the most critical part of a good simulation.
Due to their ease of use and low computational cost, group contribution methods (e.g. UNIFAC) are in widespread use despite their well-documented lack of accuracy across application areas. Quantum chemical methods have gained traction but continue to be limited by the need for computational power and expert users. Also, no practically applicable quantum method consistently yields predictions within “chemical accuracy” (<1 kcal/mol difference between prediction and experiment for energetic properties, e.g. the Gibbs energy of formation) and they give no indication whether a specific prediction is reliable, even though errors can be >10 kcal/mol.
Q-props is the first development in years to drastically improve the accuracy and reliability of property predictions from quantum chemical methods. Q-props aims to consistently deliver chemical accuracy (<1 kcal/mol) and always returns a prediction-specific uncertainty, so you know how much you can trust your simulation. Q-props is also easy to use; it will interface with leading process simulation tools, performs heavy calculations in the cloud and handles all expert choices.
Q-props achieves its prediction accuracy by combining experimental data and quantum calculations with an error cancelling AI. The AI is optimized for highest possible accuracy while keeping all information relevant to get the prediction specific uncertainty.
Example: Heat of formation for ethylbenzene and tert-butanol
The table below shows the heat of formation of ethylbenzene and tert-butanol as predicted using a group contribution method (Joback), quantum chemistry (G3MP2), and Q-props, as well as the three experimental data points available from NIST:
||ΔfH, 25oC [kJ/mol]
Q-props is the only method that gives an accurate prediction in both cases. Group contribution yields chemical accuracy in the case of ethylbenzene, but is far off in the case of tert-butanol, whereas the quantum method G3MP2 gives good results in the case of tert-butanol but is significantly off for ethylbenzene. Notably, only Q-props gives an uncertainty estimate, deriving directly from the method as visualized above.
In these cases, intelligently using quantum chemistry and experimental data, Q-props gives consistently accurate predictions and specific uncertainties. Q-props also gives a clear indication of what experimental data to trust - and what not to trust - when such data is available but contradictory. It is important to note that the experimental data is only shown to benchmark the different prediction methods and highlight how Q-props can guide what data to trust. The shown data is not used to make the predictions - only data of other compounds is used for predictions.
The results are produced with a proof-of-concept tool developed by Hafnium Labs in 2017. While this tool only predicted heat of formation properties, we are currently developing a prototype tool that can predict a wide range of pure component properties. Please contact us if you are interested in testing this prototype which will be accessible through a web interface when ready.