论文标题
图形神经网络用于温度依赖性活性系数在离子液体中溶质的溶质预测
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
论文作者
论文摘要
离子液体（IL）是可持续过程的重要溶剂，并且需要预测IL中溶质的活性系数（AC）。最近，矩阵完成方法（MCM），变压器和图神经网络（GNN）在预测二元混合物的AC方面表现出很高的精度，例如宇宙RS和UNIFAC优于公认的模型。 GNN在这里特别有希望，因为他们学习了分子图到特性关系，而无需预处理，通常是变压器所需的，并且与MCMS不同，适用于不包括训练中不包括的分子。但是，对于ILS，目前缺少GNN应用程序。在此，我们提出了一个GNN，以预测IL中溶质的温度依赖性无限稀释液。我们在包括40,000多个AC值的数据库上训练GNN，并将其与最先进的MCM进行比较。 GNN和MCM实现了类似的高预测性能，GNN还可以为培训期间未考虑的包含IL和溶质的溶液的AC提供高质量的预测。
Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures, superior to well-established models, e.g., COSMO-RS and UNIFAC. GNNs are particularly promising here as they learn a molecular graph-to-property relationship without pretraining, typically required for transformers, and are, unlike MCMs, applicable to molecules not included in training. For ILs, however, GNN applications are currently missing. Herein, we present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training.
