A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning model that is robust to overfitting. This method can be applied to predict various properties of a liquid system, including viscosity or surface tension, based on molecular features drawn from the ab initio calculated free energy of solvation. Furthermore, we propose a modified kernel model that includes Arrhenius temperature dependence to incorporate theoretical principles and diminish extreme nonlinearity in the model. The modified kernel model demonstrated significant improvements in certain scenarios and possible extensions to various theoretical concepts of molecular systems.

中文翻译：

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液体特性的协同建模：将神经网络衍生的分子特征与修改后的内核模型集成。


将机器学习应用于计算化学的一个重大挑战是可用实验数据的稀缺，特别是考虑到当代机器学习模型日益复杂。为了解决这个问题，我们引入了一种方法，该方法从基于复杂神经网络的模型中派生分子特征，并将其应用于更简单的传统机器学习模型，该模型对过拟合具有鲁棒性。该方法可用于根据从头计算的溶剂化自由能中得出的分子特征来预测液体系统的各种特性，包括粘度或表面张力。此外，我们提出了一个改进的核模型，其中包括 Arrhenius 温度依赖性，以结合理论原理并减少模型中的极端非线性。修改后的核模型在某些场景中表现出显着改进，并可能扩展到分子系统的各种理论概念。