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 High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

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 ◽  
Tight Binding ◽  
Computational Cost ◽  
Quality Data ◽  
Minor Variation ◽  
Screening Approach

<p>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.</p>

scholarly journals Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

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, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> 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.

scholarly journals Assessing the stability of Pd-exchanged sites in zeolites with the aid of a high throughput quantum chemistry workflow

2021 ◽  
Author(s):  
Hassan Aljama ◽  
Martin Head-Gordon ◽  
Alexis Bell
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Active Sites ◽  
Density Functional ◽  
Functional Materials ◽  
No Adsorption ◽  
Functional Theory ◽  
Wide Range ◽  
The Stability ◽  
Shed Light

Abstract Cation exchanged-zeolites are functional materials with a wide range of applications from catalysis to sorbents. They present a challenge for computational studies using density functional theory due to the numerous possible active sites. From Al configuration, to placement of extra framework cation(s), to potentially different oxidation states of the cation, accounting for all these possibilities is not trivial. To make the number of calculations more tractable, most studies focus on a few active sites. We attempt to go beyond these limitations by implementing a workflow for a high throughput screening, designed to systematize the problem and exhaustively search for feasible active sites. We use Pd-exchanged CHA and BEA to illustrate the approach. After conducting thousands of individual calculations, we identify the sites most favorable for the Pd cation and discuss the results in detail. The high throughput screening identifies many energetically favorable sites that are non-trivial. Lastly, we employ these results to examine NO adsorption in Pd-exchanged CHA, which is a promising passive NOx adsorbent (PNA) during the cold start of automobiles. The results shed light on critical active sites for NOx capture that were not previously studied.

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 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 ◽  
Tight Binding ◽  
Computational Cost ◽  
Quality Data ◽  
Minor Variation ◽  
Screening Approach

<p>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.</p>

scholarly journals Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

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, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> 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.

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.

Sign in / Sign up

Export Citation Format

Share Document