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 31,618 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.

Virtual screening of organic quinones as cathode materials for sodium-ion batteries

“…A far-reaching effect in the electron-withdrawing and electron-donating ability of functional groups was intended for in order to induce local changes in the electron density on the redox-active carbonyl units of the quinone-based molecules. 45–49 Thus, to cover a sizable chemical space when searching for redox active SIB cathode candidates, we considered nine different R-groups for the functionalization of all of the 170 backbone molecules. These R-groups were –CN, –CF 3 , –COOCH 3 , –F, –Cl, –Br, –CH 3 , –OCH 3 , and –NH 2 .…”

“…According to this, all the hydrogen atoms that are bonded to the molecular backbone carbon atoms have been consistently substituted with a particular R-group. A far-reaching effect in the electron-withdrawing and electron-donating ability of functional groups was intended for in order to induce local changes in the electron density on the redoxactive carbonyl units of the quinone-based molecules [45][46][47][48][49] . Thus, to cover a sizable chemical space when searching for redox active SIB cathode candidates, we considered nine different R-groups for the functionalization of all of the 170 backbone molecules.…”

“…in the electron density on the redox-active carbonyl units of the quinone-based molecules. [45][46][47][48][49] Thus, to cover a sizable chemical space when searching for redox active SIB cathode candidates, we considered nine different R-groups for the functionalization of all of the 170 backbone molecules. These R-groups were -CN, -CF 3 , -COOCH 3 , -F, -Cl, -Br, -CH 3 , -OCH 3 , and -NH 2 .…”

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

“…Very recently, Er and coauthors tested several molecular representations and machine learning models for predicting the DFT-computed aqueous one-step two-electron two-proton redox reaction energies of more than 15,000 redox-active molecule pairs from the RedDB database . They found that structural fingerprints outperform physicochemical descriptors and natural language processing embeddings in encoding the redox chemistry.…”

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

“…Such a database is unique for varying oligonucleotides, even though databases of many MM and QM properties exist for other types of molecules, many reported in the same, Scientific Data, journal. Examples of other QM-based datasets published in the same spirit are physico-chemical properties of 31,618 electroactive molecules for development of aqueous redox flow batteries 15 , optimised molecular geometries and thermodynamic data of more than 665,000 biologically and pharmacologically relevant molecules 16 , electronic charge density of crystalline materials from Materials Project database 17 , molecular conformations of 450,000 small- and mid-sized organic molecules 18 , molecular geometries and spectral properties of 61,489 crystal-forming organic molecules 19 , equilibrium conformations for small organic molecules 20 , QM calculations of over 200,000 organic radical species and 40,000 associated closed-shell molecules 21 , all-atom force-field parameters, molecular dynamics trajectories, QM properties, and curated physicochemical descriptors of more than 300 antimicrobial compounds 22 , excited state information of 173,000 organic molecules 23 , conformational energies and geometries of di- and tripeptides 24 , and QM structures and properties of 134,000 small organic molecules 25 .…”