Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches

Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad Risko

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed.

Original languageEnglish
Pages (from-to)203-213
Number of pages11
JournalChemical Science
Volume14
Issue number1
DOIs
StatePublished - Nov 17 2022

Bibliographical note

Funding Information:
This work was sponsored at University of Kentucky (UK) by the National Science Foundation in part through the Designing Materials to Revolutionize and Engineer our Future (NSF DMREF) program under award number DMR-1627428 and UK and Iowa State University (ISU) through Cooperative Agreement 2019574. P. S. acknowledges support from the Arnold O. and Mabel Beckman Foundation through the Beckman Scholars Program. ISU also acknowledges support from the Office of Naval Research (ONR) through award number N00014-19-12453. We acknowledge the UK Center for Computational Sciences and Information Technology Services Research Computing for their fantastic support and collaboration, and use of the Lipscomb Compute Cluster and associated research computing resources. Computational resources were also provided through the NSF Extreme Science and Engineering Discovery Environment (XSEDE) program on Stampede2 through allocation award TG-CHE200119.

Publisher Copyright:
© 2023 The Royal Society of Chemistry.

ASJC Scopus subject areas

  • Chemistry (all)

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