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

We propose a general high-throughput virtual 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.

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

10.26434/chemrxiv.6181841 ◽

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 ◽

Tight Binding ◽

Computational Cost ◽

Quality Data ◽

Minor Variation ◽

Screening Approach

We propose a general high-throughput virtual 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.

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

Acta Crystallographica Section B Structural Science Crystal Engineering and Materials ◽

10.1107/s2052520618015718 ◽

2019 ◽

Vol 75 (1) ◽

pp. 18-33 ◽

Cited By ~ 35

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.

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Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

10.26434/chemrxiv.12115944.v1 ◽

2020 ◽

Author(s):

Chenru Duan ◽

Fang Liu ◽

Aditya Nandy ◽

Heather Kulik

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Density Functional ◽

Correlation Energy ◽

Low Cost ◽

Small Data ◽

Data Sets ◽

Strongly Correlated ◽

Heavy Atoms ◽

Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %Ecorr. None of the DFT-based diagnostics are nearly as predictive of %Ecorr as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

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

10.26434/chemrxiv.12931226 ◽

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 in silico approach to accelerate both the singly and the high-throughput computational screening studies, therewithal considering the level of accuracy vs measured electrochemical data, that is principally applicable for the discovery of efficient, alloxazine-derived organic compounds for energy storage in aqueous redox flow batteries.

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

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.8b00256 ◽

2018 ◽

Vol 58 (12) ◽

pp. 2450-2459 ◽

Cited By ~ 16

Author(s):

Liam Wilbraham ◽

Enrico Berardo ◽

Lukas Turcani ◽

Kim E. Jelfs ◽

Martijn A. Zwijnenburg

Keyword(s):

Conjugated Polymers ◽

High Throughput ◽

High Throughput Screening ◽

Optoelectronic Properties ◽

Screening Approach

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Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

10.26434/chemrxiv.12115944 ◽

2020 ◽

Author(s):

Chenru Duan ◽

Fang Liu ◽

Aditya Nandy ◽

Heather Kulik

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Density Functional ◽

Correlation Energy ◽

Low Cost ◽

Small Data ◽

Data Sets ◽

Strongly Correlated ◽

Heavy Atoms ◽

Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %Ecorr. None of the DFT-based diagnostics are nearly as predictive of %Ecorr as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

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

10.26434/chemrxiv.12931226.v1 ◽

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 in silico approach to accelerate both the singly and the high-throughput computational screening studies, therewithal considering the level of accuracy vs measured electrochemical data, that is principally applicable for the discovery of efficient, alloxazine-derived organic compounds for energy storage in aqueous redox flow batteries.

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

Scientific Reports ◽

10.1038/s41598-021-83605-2 ◽

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.

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