Predicting the physico-chemical properties of pure substances and mixtures is
a central task in thermodynamics. Established prediction methods range from
fully physics-based ab-initio calculations, which are only feasible for very
simple systems, over descriptor-based methods that use some information on the
molecules to be modeled together with fitted model parameters (e.g.,
quantitative-structure-property relationship methods or classical group
contribution methods), to representation-learning methods, which may, in
extreme cases, completely ignore molecular descriptors and extrapolate only
from existing data on the property to be modeled (e.g., matrix completion
methods). In this work, we propose a general method for combining molecular
descriptors with representation learning using the so-called expectation
maximization algorithm from the probabilistic machine learning literature,
which uses uncertainty estimates to trade off between the two approaches. The
proposed hybrid model exploits chemical structure information using graph
neural networks, but it automatically detects cases where structure-based
predictions are unreliable, in which case it corrects them by
representation-learning based predictions that can better specialize to unusual
cases. The effectiveness of the proposed method is demonstrated using the
prediction of activity coefficients in binary mixtures as an example. The
results are compelling, as the method significantly improves predictive
accuracy over the current state of the art, showcasing its potential to advance
the prediction of physico-chemical properties in general.

利用分子描述符和表示学习相结合的方法，通过结合概率机器学习文献中的期望最大化算法，自动检测并纠正基于结构的预测不可靠的情况，从而显著提高了对二元混合物中活度系数的预测准确性，展示了在物理化学性质预测中的潜力。

平衡分子信息与实证数据在物理 - 化学性质预测中的应用

Balancing Molecular Information and Empirical Data in the Prediction of  Physico-Chemical Properties

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.

建立一种概率矩阵分解模型，用于预测任意二元混合物中活度系数，该模型不需使用任何已知的物理描述符，优于目前已经发展三十年的业内领先方法，可用于预测混合物的物理化学性质。

矩阵完整化在热力学中的应用：活度系数的机器学习预测

Machine Learning in Thermodynamics: Prediction of Activity Coefficients  by Matrix Completion

Prediction of molecular properties, including physico-chemical properties, is
a challenging task in chemistry. Herein we present a new state-of-the-art
multitask prediction method based on existing graph neural network models. We
have used different architectures for our models and the results clearly
demonstrate that multitask learning can improve model performance.
Additionally, a significant reduction of variance in the models has been
observed. Most importantly, datasets with a small amount of data points reach
better results without the need of augmentation.

提出了一种基于图神经网络模型的最新、高效的多任务预测方法，结果表明，多任务学习可以提高模型性能，特别是数据点较少的数据集可以不需要数据增强，便能获得较好的效果，并且能显著减小模型的方差。