Jirasek Fabian, Alves Rodrigo A S, Damay Julie, Vandermeulen Robert A, Bamler Robert, Bortz Michael, Mandt Stephan, Kloft Marius, Hasse Hans
Department of Computer Science , University of California , Irvine , California 92697 , United States.
Laboratory of Engineering Thermodynamics (LTD) , TU Kaiserslautern , 67663 Kaiserslautern , Germany.
J Phys Chem Lett. 2020 Feb 6;11(3):981-985. doi: 10.1021/acs.jpclett.9b03657. Epub 2020 Jan 23.
Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.
活度系数是衡量液体混合物非理想性的一个指标，是化学工程中的一个关键性质，与化学平衡和相平衡以及传输过程的建模相关。尽管有成千上万种二元混合物的实验数据，但对于许多迄今尚未研究的相关混合物，仍需要预测方法来计算其活度系数。在本报告中，我们提出了一种概率矩阵分解模型，用于预测任意二元混合物中的活度系数。尽管没有使用所考虑组分的物理描述符，但我们的方法优于经过三十多年改进的现有最佳方法，同时所需的训练工作量要少得多。这为预测二元混合物物理化学性质的新方法开辟了前景，有可能彻底改变化学工程中的建模和模拟。
