RedDB, a Computational Database of Electroactive Molecules for Aqueous Redox Flow Batteries

Abstract

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,677 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: i)chemical library generation, ii) molecular property prediction based on quantum chemical calculations, iii) aqueous solubility prediction using machine learning, and iv) data processing and database creation, have been described.

ChemPlot, a Python library for chemical space visualization

“…The traditionally used form is the structural similarity that attempts to evaluate the similarity using all possible aspects of the given molecular datasets and without taking the target property into account. This approach is truly suitable when one wants to have a general overview of the dataset 30 . However, structural similarity often fails when searching for new molecules with target properties.…”

ChemPlot, a Python library for chemical space visualization

“…The traditionally used, structural similarity attempts to evaluate the similarity using all possible aspects of the given molecular datasets and without taking the target property into account. This approach is very suitable when one wants to have a general overview of the dataset 29 . However, structural similarity often fails when searching for new molecules with target properties.…”

Machine learning for flow batteries: opportunities and challenges

“… 70 The construction of the molecular structure–property database using the HTVS strategy for ORASs mainly includes three stages: molecule library generation, molecule structure and property generation, and database creation. 71 The process for the development of RedDB is shown in Fig. 4 .…”

“…How to share the data more efficiently is another challenge. Currently, there are only some open sources of chemoinformatics databases, 71,107 and the validity and amount are limited. In addition, innovative ML algorithms, including clustering, principal component analysis (PCA), autoencoder (AE), generative adversarial network (GAN) and meta-learning models like neural Turing machines 108 and imitation learning algorithms, 109 are promising solutions for key materials design in FBs.…”