论文标题
热力学中的机器学习：通过矩阵完成对活动系数的预测
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
论文作者
论文摘要
活性系数是衡量液体混合物的非理想性的一种量度，是化学工程的关键特性，与化学和相位平衡以及运输过程相关。尽管可以使用数千种二进制混合物的实验数据，但仍需要进行预测方法来计算许多尚未探讨尚未探索的相关混合物中的活动系数。在本报告中，我们提出了一个概率矩阵分解模型，用于预测任意二进制混合物中的活性系数。尽管没有使用对所考虑组件的物理描述符，但我们的方法的表现优于最先进的方法，该方法已在三十年中进行了完善，同时需要更少的培训工作。这为预测二元混合物的物理化学特性的新方法打开了观点，并有可能彻底改变化学工程中的建模和模拟。
Activity coefficients, which are a measure of the non-ideality 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 physico-chemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.
