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, 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.

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

“…[50,51] HTCS is specifically adapted to unravel quantitative structure-property relationships, which are essential for identifying materials that potentially meet RFB requirements. [52] By combining forward and inverse mapping principles, HTCS contributes to reducing the considerable initial costs associated with experimental high-throughput techniques in RFB's complex material space. [51d] DFT and molecular dynamics (MD) are critical computational tools that accelerate the experimental HTS for RFBs.…”

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

“…The MolGAT model, introduced by Chaka et al , 39 is a type of GNN that learns molecular structures, bond attributes, and atomic properties using attention-based message passing techniques. When compared to other graph-based models, this model has demonstrated promising performance in predicting the redox potential of organic molecules.…”

“…When compared to other graph-based models, this model has demonstrated promising performance in predicting the redox potential of organic molecules. 39 In this particular study, the MolGAT model was trained using the AqSolDB dataset to accurately predict the aqueous solubility of various organic molecules. The mathematical representation of this model is as follows:…”

“…GNNs are capable of learning and generalizing from molecular graph-structured data, making them well-suited for predicting molecular properties such as redox potential, as demonstrated by Chaka et al 39 This has important implications for using the GNN model for predicting the aqueous solubility of new materials for energy storage applications. Consequently, the application of GNNs to predict organic molecule solubility is crucial for the development of redox flow batteries with high efficiency and reliability, as well as numerous other uses in various industries.…”

“…Consequently, the application of GNNs to predict organic molecule solubility is crucial for the development of redox flow batteries with high efficiency and reliability, as well as numerous other uses in various industries. In this study, we used MolGAT 39 together with a commonly used molecular graph format to train the model on the AqSolDB 15 dataset to estimate the intrinsic water solubility of the organic molecules. The aim was not to develop a new modelling framework but rather to predict and classify compounds that could potentially be beneficial as redox-active materials that had already been screened in our previous work.…”