Synergy of Machine Learning and Density Functional Theory Calculations for Predicting Experimental Lewis Base Affinity and Lewis Polybase Binding Atoms

01 February 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and Machine Learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from Density Functional Theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.


Lewis acid-base adducts
Density Functional Theory
Machine Learning
Graph Neural Network
Lewis base affinity

Supplementary materials

Supplementary Materials
The file contains supplementary figures that are mentioned in the main paper.


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