Title: High-Throughput Screening of Promising Redox-Active Molecules with MolGAT

Abstract

Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from –4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB,more » QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as –2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing.« less

Authors:

Chaka, Mesfin Diro  ^[1];

• Search DOE PAGES for author "Chaka, Mesfin Diro"
• Search DOE PAGES for ORCID "0000-0003-4248-9644"

Geffe, Chernet Amente ^[2]; Rodriguez, Alex  ^[3];

• Search DOE PAGES for author "Rodriguez, Alex"
• Search DOE PAGES for ORCID "0000-0002-0213-6695"

Seriani, Nicola  ^[3];

• Search DOE PAGES for author "Seriani, Nicola"
• Search DOE PAGES for ORCID "0000-0001-5680-9747"

Wu, Qin  ^[4];

• Search DOE PAGES for author "Wu, Qin"
• Search DOE PAGES for ORCID "0000-0001-6350-6672"

Mekonnen, Yedilfana Setarge  ^[5]

• Search DOE PAGES for author "Mekonnen, Yedilfana Setarge"
• Search DOE PAGES for ORCID "0000-0001-9331-7370"

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+ Show Author Affiliations

1. Department of Physics, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia, Computational Data Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia,
2. Department of Physics, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia
3. The Abdus Salam International Centre for Theoretical Physics(ICTP) Condensed Matter and Statistical Physics Section, 34100 Trieste, Italy,
4. Brookhaven National Laboratory, Center for Functional Nanomaterials, Upton New York 11973, United States,
5. Center for Environmental Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia,

Publication Date:

Fri Jun 30 00:00:00 EDT 2023

Research Org.:

Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:

1987931

Alternate Identifier(s):

OSTI ID: 1989170; OSTI ID: 1992872

Report Number(s):

BNL-224611-2023-JAAM
Journal ID: ISSN 2470-1343

Grant/Contract Number:  

SC0012704

Resource Type:

Published Article

Journal Name:

ACS Omega

Additional Journal Information:

Journal Name: ACS Omega Journal Volume: 8 Journal Issue: 27; Journal ID: ISSN 2470-1343

Publisher:

American Chemical Society

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Drug discovery; Materials; Molecular modeling; Molecules; Redox reactions; 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY