论文标题
杂交物理和数据驱动的物理化学特性的预测方法
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
论文作者
论文摘要
我们提出了一种杂交物理和数据驱动方法来预测物理化学特性的通用方法。该方法将物理方法的预测“提炼”到先前的模型中，并将其与使用贝叶斯推断的稀疏实验数据相结合。我们采用新方法来预测无限稀释时的活动系数，并与数据驱动和物理基线以及从机器学习文献中建立的集合方法相比，获得了显着改进。
We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the data-driven and physical baselines and established ensemble methods from the machine learning literature.
