scholarly journals A Quantitative Evaluation of Computational Methods to Accelerate the Study of Alloxazine-Derived Electroactive Compounds for Energy Storage

2020 ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Computational Methods ◽  
High Throughput ◽  
Density Functional ◽  
Computational Cost ◽  
Orbital Energy ◽  
Redox Potentials ◽  
Redox Flow Batteries ◽  
Computational Screening ◽  
Flow Batteries

Alloxazines are a promising class of organic electroactive molecules for application in aqueous redox flow batteries. Preliminary studies show that structural modifications of alloxazines with electron-donating and/or -withdrawing functional groups help in tuning of their redox properties. High-throughput computational screening enables rational and time-efficient discovery of functional compounds. The effectiveness of high-throughput computational screening efforts is strongly dependent on the accuracy and speed at which the performance descriptors are estimated for a large pool of candidate compounds. Here, we performed a quantitative study to assess the performance of computational methods, including the forcefield based molecular mechanics, semi-empirical quantum mechanics, density functional based tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of electroactive alloxazines. We compared the performances of various energy-based descriptors, including the redox reaction energy and the frontier orbital energies of the reactant and product molecules. We found that the lowest unoccupied molecular orbital energy of the reactant molecules is the best performing descriptor for the alloxazines, which is in contrast to other classes of molecules, such as quinones that we reported earlier. Importantly, we present a flexible<i> in silico</i> approach to accelerate both the singly and the high-throughput computational screening studies, therewithal considering the level of accuracy <i>vs</i> measured electrochemical data, that is principally applicable for the discovery of efficient, alloxazine-derived organic compounds for energy storage in aqueous redox flow batteries.

scholarly journals A Quantitative Evaluation of Computational Methods to Accelerate the Study of Alloxazine-Derived Electroactive Compounds for Energy Storage

2020 ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Computational Methods ◽  
High Throughput ◽  
Density Functional ◽  
Computational Cost ◽  
Orbital Energy ◽  
Redox Potentials ◽  
Redox Flow Batteries ◽  
Computational Screening ◽  
Flow Batteries

Alloxazines are a promising class of organic electroactive molecules for application in aqueous redox flow batteries. Preliminary studies show that structural modifications of alloxazines with electron-donating and/or -withdrawing functional groups help in tuning of their redox properties. High-throughput computational screening enables rational and time-efficient discovery of functional compounds. The effectiveness of high-throughput computational screening efforts is strongly dependent on the accuracy and speed at which the performance descriptors are estimated for a large pool of candidate compounds. Here, we performed a quantitative study to assess the performance of computational methods, including the forcefield based molecular mechanics, semi-empirical quantum mechanics, density functional based tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of electroactive alloxazines. We compared the performances of various energy-based descriptors, including the redox reaction energy and the frontier orbital energies of the reactant and product molecules. We found that the lowest unoccupied molecular orbital energy of the reactant molecules is the best performing descriptor for the alloxazines, which is in contrast to other classes of molecules, such as quinones that we reported earlier. Importantly, we present a flexible<i> in silico</i> approach to accelerate both the singly and the high-throughput computational screening studies, therewithal considering the level of accuracy <i>vs</i> measured electrochemical data, that is principally applicable for the discovery of efficient, alloxazine-derived organic compounds for energy storage in aqueous redox flow batteries.

scholarly journals A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Density Functional ◽  
Tight Binding ◽  
Computational Cost ◽  
Redox Potentials ◽  
Storage Compounds ◽  
Computational Screening ◽  
Chemical Descriptor ◽  
Lowest Unoccupied Molecular Orbital ◽  
Semi Empirical

AbstractAlloxazines are a promising class of organic electroactive compounds for application in aqueous redox flow batteries (ARFBs), whose redox properties need to be tuned further for higher performance. High-throughput computational screening (HTCS) enables rational and time-efficient study of energy storage compounds. We compared the performance of computational chemistry methods, including the force field based molecular mechanics, semi-empirical quantum mechanics, density functional tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of alloxazines. Various energy-based descriptors, including the redox reaction energies and the frontier orbital energies of the reactant and product molecules, were considered. We found that the lowest unoccupied molecular orbital (LUMO) energy of the reactant molecules is the best performing chemical descriptor for alloxazines, which is in contrast to other classes of energy storage compounds, such as quinones that we reported earlier. Notably, we present a flexible in silico approach to accelerate both the singly and the HTCS studies, therewithal considering the level of accuracy versus measured electrochemical data, which is readily applicable for the discovery of alloxazine-derived organic compounds for energy storage in ARFBs.

scholarly journals Comparison of computational chemistry methods for the discovery of quinone-based electroactive compounds for energy storage

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Prediction Accuracy ◽  
Density Functional ◽  
Single Point ◽  
Computational Cost ◽  
Systematic Evaluation ◽  
Redox Potentials ◽  
Implicit Solvation ◽  
Point Energy ◽  
Computational Screening

AbstractHigh-throughput computational screening (HTCS) is a powerful approach for the rational and time-efficient design of electroactive compounds. The effectiveness of HTCS is dependent on accuracy and speed at which the performance descriptors can be estimated for possibly millions of candidate compounds. Here, a systematic evaluation of computational methods, including force field (FF), semi-empirical quantum mechanics (SEQM), density functional based tight binding (DFTB), and density functional theory (DFT), is performed on the basis of their accuracy in predicting the redox potentials of redox-active organic compounds. Geometry optimizations at low-level theories followed by single point energy (SPE) DFT calculations that include an implicit solvation model are found to offer equipollent accuracy as the high-level DFT methods, albeit at significantly lower computational costs. Effects of implicit solvation on molecular geometries and SPEs, and their overall effects on the prediction accuracy of redox potentials are analyzed in view of computational cost versus prediction accuracy, which outlines the best choice of methods corresponding to a desired level of accuracy. The modular computational approach is applicable for accelerating the virtual studies on functional quinones and the respective discovery of candidate compounds for energy storage.

scholarly journals Comparison of Computational Chemistry Methods for the Discovery of Quinone-Based Electroactive Compounds for Energy Storage

2020 ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Prediction Accuracy ◽  
Density Functional ◽  
Single Point ◽  
Computational Cost ◽  
Systematic Evaluation ◽  
Redox Potentials ◽  
Implicit Solvation ◽  
Point Energy ◽  
Computational Screening

High-throughput computational screening (HTCS) is an approach that can enable rational and time-efficient discovery of electroactive compounds. The effectiveness of HTCS is dependent on the accuracy and speed at which the performance descriptors can be estimated for possibly millions of candidate compounds. Here, a systematic evaluation of computational methods, including force field (FF), semi-empirical quantum mechanics (SEQM), density functional based tight binding (DFTB), and density functional theory (DFT), is performed on the basis of their accuracy in predicting the redox potentials of redox-active organic compounds. Geometry optimizations at lower level theories followed by single point energy (SPE) DFT calculations including an implicit solvation model are found to offer equipollent accuracy as the higher level DFT methods, albeit at significantly lower computational costs. Effects of implicit solvation on molecular geometries and SPEs, and their overall effects on the prediction accuracy of redox potentials are analyzed in view of computational cost versus prediction accuracy, which outlines the best choice of methods corresponding to a desired level of accuracy. The modular computational approach presented here is expected to be applicable for accelerating virtual studies on functional quinones and the respective discovery of candidate compounds for energy storage.

scholarly journals Comparison of Computational Chemistry Methods for the Discovery of Quinone-Based Electroactive Compounds for Energy Storage

2020 ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Prediction Accuracy ◽  
Density Functional ◽  
Single Point ◽  
Computational Cost ◽  
Systematic Evaluation ◽  
Redox Potentials ◽  
Implicit Solvation ◽  
Point Energy ◽  
Computational Screening

High-throughput computational screening (HTCS) is an approach that can enable rational and time-efficient discovery of electroactive compounds. The effectiveness of HTCS is dependent on the accuracy and speed at which the performance descriptors can be estimated for possibly millions of candidate compounds. Here, a systematic evaluation of computational methods, including force field (FF), semi-empirical quantum mechanics (SEQM), density functional based tight binding (DFTB), and density functional theory (DFT), is performed on the basis of their accuracy in predicting the redox potentials of redox-active organic compounds. Geometry optimizations at lower level theories followed by single point energy (SPE) DFT calculations including an implicit solvation model are found to offer equipollent accuracy as the higher level DFT methods, albeit at significantly lower computational costs. Effects of implicit solvation on molecular geometries and SPEs, and their overall effects on the prediction accuracy of redox potentials are analyzed in view of computational cost versus prediction accuracy, which outlines the best choice of methods corresponding to a desired level of accuracy. The modular computational approach presented here is expected to be applicable for accelerating virtual studies on functional quinones and the respective discovery of candidate compounds for energy storage.

scholarly journals Tuning the Performance of Aqueous Organic Redox Flow Batteries from First Principles

2020 ◽  
Author(s):  
Junting Yu ◽  
Tianshou Zhao ◽  
Ding Pan
Keyword(s):  
Energy Storage ◽  
First Principles ◽  
High Performance ◽  
Large Scale ◽  
Electrical Energy ◽  
Ab Initio Molecular Dynamics ◽  
Redox Potentials ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Entropy Effects

<div>Aqueous organic redox flow batteries have many appealing properties in the application of large-scale energy storage. The large chemical tunability of organic electrolytes shows great potential to improve the performance of flow batteries. Computational studies at the quantum-mechanics level are very useful to guide experiments, but in previous studies explicit water interactions and thermodynamic effects were ignored. Here, we applied the computational electrochemistry method based on ab initio molecular dynamics to calculate redox potentials of quinones and their derivatives. The calculated results are in excellent agreement with experimental data. We mixed side chains to tune their reduction potentials, and found that solvation interactions and entropy effects play a significant role in side-chain engineering. Based on our calculations, we proposed several high-performance negative and positive electrolytes. Our first-principles study paves the way towards the development of large-scale and sustainable electrical energy storage.</div>

scholarly journals New insights into phenazine-based organic redox flow batteries by using high-throughput DFT modelling

2020 ◽  
Vol 4 (11) ◽  
pp. 5513-5521 ◽  
Author(s):  
Carlos de la Cruz ◽  
Antonio Molina ◽  
Nagaraj Patil ◽  
Edgar Ventosa ◽  
Rebeca Marcilla ◽  
...  
Keyword(s):  
Dft Calculations ◽  
High Throughput ◽  
Aqueous Media ◽  
Redox Potentials ◽  
Structure Property ◽  
Redox Flow Batteries ◽  
Structure Property Relationships ◽  
Flow Batteries ◽  
Dft Modelling ◽  
Interesting Structure

DFT calculations reveal interesting structure–property relationships of the redox potentials of phenazines in non-aqueous media.

SoftBV – a software tool for screening the materials genome of inorganic fast ion conductors

2019 ◽  
Vol 75 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Haomin Chen ◽  
Lee Loong Wong ◽  
Stefan Adams
Keyword(s):  
Force Field ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Computational Cost ◽  
Lewis Base ◽  
Computational Screening ◽  
Wide Range ◽  
Dft Simulations

The identification of materials for advanced energy-storage systems is still mostly based on experimental trial and error. Increasingly, computational tools are sought to accelerate materials discovery by computational predictions. Here are introduced a set of computationally inexpensive software tools that exploit the bond-valence-based empirical force field previously developed by the authors to enable high-throughput computational screening of experimental or simulated crystal-structure models of battery materials predicting a variety of properties of technological relevance, including a structure plausibility check, surface energies, an inventory of equilibrium and interstitial sites, the topology of ion-migration paths in between those sites, the respective migration barriers and the site-specific attempt frequencies. All of these can be predicted from CIF files of structure models at a minute fraction of the computational cost of density functional theory (DFT) simulations, and with the added advantage that all the relevant pathway segments are analysed instead of arbitrarily predetermined paths. The capabilities and limitations of the approach are evaluated for a wide range of ion-conducting solids. An integrated simple kinetic Monte Carlo simulation provides rough (but less reliable) predictions of the absolute conductivity at a given temperature. The automated adaptation of the force field to the composition and charge distribution in the simulated material allows for a high transferability of the force field within a wide range of Lewis acid–Lewis base-type ionic inorganic compounds as necessary for high-throughput screening. While the transferability and precision will not reach the same levels as in DFT simulations, the fact that the computational cost is several orders of magnitude lower allows the application of the approach not only to pre-screen databases of simple structure prototypes but also to structure models of complex disordered or amorphous phases, and provides a path to expand the analysis to charge transfer across interfaces that would be difficult to cover by ab initio methods.

scholarly journals A High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers

2018 ◽  
Author(s):  
Liam Wilbraham ◽  
Enrico Berardo ◽  
Lukas Turcani ◽  
Kim Jelfs ◽  
Martijn Zwijnenburg
Keyword(s):  
Conjugated Polymers ◽  
Electronic Properties ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Computational Cost ◽  
Quality Data ◽  
Minor Variation ◽  
Computational Screening ◽  
Screening Approach

<p>We propose a general high-throughput computational screening approach for the optical and electronic properties of conjugated polymers. This approach makes use of the recently developed xTB family of low-computational-cost density functional tight-binding methods from Grimme and co-workers, calibrated here to (TD-)DFT data computed for a representative diverse set of (co-)polymers. Parameters drawn from the resulting calibration using a linear model can then be applied to the xTB derived results for new polymers, thus generating near DFT-quality data with orders of magnitude reduction in computational cost. As a result, after an initial computational investment for calibration, this approach can be used to quickly and accurately screen on the order of thousands of polymers for target applications. We also demonstrate that the (opto)electronic properties of the conjugated polymers show only a very minor variation when considering different conformers and that the results of high-throughput screening are therefore expected to be relatively insensitive with respect to the conformer search methodology applied.</p>

scholarly journals Tuning the Performance of Aqueous Organic Redox Flow Batteries from First Principles

2020 ◽  
Author(s):  
Junting Yu ◽  
Tianshou Zhao ◽  
Ding Pan
Keyword(s):  
Energy Storage ◽  
First Principles ◽  
High Performance ◽  
Large Scale ◽  
Electrical Energy ◽  
Ab Initio Molecular Dynamics ◽  
Redox Potentials ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Entropy Effects

<div>Aqueous organic redox flow batteries have many appealing properties in the application of large-scale energy storage. The large chemical tunability of organic electrolytes shows great potential to improve the performance of flow batteries. Computational studies at the quantum-mechanics level are very useful to guide experiments, but in previous studies explicit water interactions and thermodynamic effects were ignored. Here, we applied the computational electrochemistry method based on ab initio molecular dynamics to calculate redox potentials of quinones and their derivatives. The calculated results are in excellent agreement with experimental data. We mixed side chains to tune their reduction potentials, and found that solvation interactions and entropy effects play a significant role in side-chain engineering. Based on our calculations, we proposed several high-performance negative and positive electrolytes. Our first-principles study paves the way towards the development of large-scale and sustainable electrical energy storage.</div>

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