论文标题
MLSOLV-A：一种基于机器学习的新型预测，对成对原子相互作用的溶剂化自由能的预测
MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions
论文作者
论文摘要
机器学习及其应用的最新进展导致了重要的化学特性的各种结构 - 特性关系模型的发展，而溶剂化自由能是其中之一。在这里，我们介绍了一种基于ML的新型溶剂化模型，该模型可以计算成对原子相互作用的溶剂化能。提出的模型的新颖性由一个简单的结构组成：两个编码函数提取给定化学结构的原子特征向量，而两个原子特征之间的内部产物计算它们的相互作用。 6,493次实验测量结果的结果可实现出色的性能和可转移性，以扩大培训数据，这是由于其溶剂特异性的。对交互作用图的分析表明，我们的模型对溶剂化能量的群体贡献具有很大的潜力，这使我们相信该模型不仅提供了预测的目标属性，而且还为我们提供了更详细的物理化学见解。
Recent advances in machine learning and their applications have lead to the development of diverse structure-property relationship models for crucial chemical properties, and 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 two atomistic features calculates their interactions. The results on 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the interaction map shows there is a great potential that our model reproduces group contributions on the solvation energy, which makes us believe that the model not only provides the predicted target property but also gives us more detailed physicochemical insights.
