Abstract
Current study results on Machine Learning have been published. According to news reporting from Dortmund, Germany, by NewsRx journalists, research stated, “State of the art thermodynamic models, such as the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), require a thorough parametrization (three pure-component parameters for nonassociating molecules) of the molecules considered. In our previous work (J. Habicht, C. Brandenbusch, G. Sadowski, Fluid Phase Equilibria, 2023, 565, 113657), we introduced a Machine Learning approach for a predictive parametrization of nonassociating components.” Funders for this research include German Research Foundation (DFG), German Research Foundation (DFG). The news correspondents obtained a quote from the research from Technical University Dortmund (TU Dortmund), “Within this approach, training is performed using a Huber-loss function, comparing the ML-predicted parameter set with the original one, e.g., from literature. However, often multiple purecomponent parameter sets exist for one molecule. This fact makes the training to only one 'true' parameter set questionable. Within this work, we thus performed a detailed analysis on the fact of multiparameter set existence. We further expanded our ML-approach by developing a choice of two physics-informed loss functions that allow for the consideration of multiple 'true' parameter sets during training. Results indicate that reliable pure-component parameters have a certain orientation when plotted in the three-dimensional parameter space.”
NETL
NSTL
Amer Chemical Soc