Benchmarking DFT and Supervised Machine Learning: An Organic Semiconducting Polymer Investigation

Using a training set consisting of twenty-two well-known semiconducting organic polymers, we studied the ability of a simple linear regression supervised machine learning algorithm to accurately predict the bandgap (BG) and ionization potential (IP) of new polymers. We show that using the PBE or PW9...

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Bibliographic Details
Published in:The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory Molecules, spectroscopy, kinetics, environment, & general theory, 2024-02, Vol.128 (4), p.709-715
Main Authors: Stoltz, Kyle R., Borunda, Mario F.
Format: Article
Language:English
Subjects:
A: Structure, Spectroscopy, and Reactivity of Molecules and Clusters
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Summary:Using a training set consisting of twenty-two well-known semiconducting organic polymers, we studied the ability of a simple linear regression supervised machine learning algorithm to accurately predict the bandgap (BG) and ionization potential (IP) of new polymers. We show that using the PBE or PW91 exchange–correlation functionals and this simple linear regression, calculated BGs and IPs can be obtained with average percent errors of less than 3 and 4%, respectively. We then apply this method to predict the BG and IP of a group of new polymers composed of monomers used in the training set and their derivatives in AABB and ABAB orientations.
ISSN:1089-5639
1520-5215
DOI:10.1021/acs.jpca.3c04905