Recent advances in machine learning technologies and their applications have led to the development of diverse structure–property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights.

中文翻译：

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MLSolvA：通过机器学习从成对原子相互作用预测溶剂化自由能


机器学习技术及其应用的最新进展导致了关键化学性质的多种结构-性质关系模型的发展。溶剂化自由能就是其中之一。在这里，我们介绍了一种新颖的基于机器学习的溶剂化模型，该模型根据成对原子相互作用计算溶剂化能。该模型的新颖之处在于一个简单的架构：两个编码函数从给定的化学结构中提取原子特征向量，而两个原子特征向量之间的内积计算它们的相互作用。 6239 次实验测量的结果由于其溶剂非特定性，在扩大训练数据方面具有出色的性能和可移植性。对相互作用图的分析表明，我们的模型具有对溶剂化能产生基团贡献的巨大潜力，这表明该模型不仅提供了目标特性的预测，还提供了更详细的物理化学见解。