Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties

• URL: http://arxiv.org/abs/2202.08804v1
• Date: Thu, 17 Feb 2022 18:15:03 GMT
• Title: Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
• Authors: Fabian Jirasek, Robert Bamler, and Stephan Mandt
• Abstract summary: 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.
• Score: 19.50116420011026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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