Universal Screening Strategy for Accelerated Design of Superior Oxygen Evolution/Reduction Electrocatalysts

2021 ◽  
Author(s):  
Dong Yeon Kim ◽  
Miran Ha ◽  
Kwang S. Kim
Keyword(s):  
Oxygen Evolution ◽  
Computational Methods ◽  
High Throughput ◽  
Universal Screening ◽  
Screening Strategy ◽  
Reaction Intermediates ◽  
Vast Amount ◽  
Computational Screening

Despite advanced computational methods, it is not practical to utilize high-throughput computational screening from a vast amount of candidates for multi-step reactions due to intercorrelation between reaction intermediates. However, we...

scholarly journals Discovery of Acid-Stable Oxygen Evolution Catalysts : High-throughput Computational Screening of Equimolar Bimetallic Oxides

2020 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Oxygen Evolution ◽  
High Throughput ◽  
High Throughput Screening ◽  
Noble Metals ◽  
Cost Effective ◽  
Oxide Catalysts ◽  
Computational Screening ◽  
Energy Conversion Systems ◽  
Acid Stable ◽  
Bimetallic Oxides

Discovering acid-stable, cost-effective and active catalysts for oxygen evolution reaction (OER) is critical since this reaction is bottlenecking many electrochemical energy conversion systems. Current systems use extremely expensive iridium oxide catalysts. Identifying Ir-free or catalysts with reduced Ir-composition has been suggested as goals, but no systematic strategy to discover such catalysts has been reported. In this work, we performed high-throughput computational screening to investigate bimetalic oxide catalysts with space groups derived from those of IrO$_x$, identified promising OER catalysts predicted to satisfy all the desired properties: Co-Ir, Fe-Ir and Mo-Ir bimetallic oxides. We find that for the given crystal structures explored, it is essential to include noble metals to maintain the acid-stability, although one-to-one mixing of noble and non-noble metal oxides could keep the materials survive under the acidic conditions. Based on the calculated results, we provide insights to efficiently perform future high-throughput screening to discover catalysts with desirable properties.

scholarly journals Discovery of Acid-Stable Oxygen Evolution Catalysts : High-throughput Computational Screening of Equimolar Bimetallic Oxides

2020 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Oxygen Evolution ◽  
High Throughput ◽  
High Throughput Screening ◽  
Noble Metals ◽  
Cost Effective ◽  
Oxide Catalysts ◽  
Computational Screening ◽  
Energy Conversion Systems ◽  
Acid Stable ◽  
Bimetallic Oxides

Discovering acid-stable, cost-effective and active catalysts for oxygen evolution reaction (OER) is critical since this reaction is bottlenecking many electrochemical energy conversion systems. Current systems use extremely expensive iridium oxide catalysts. Identifying Ir-free or catalysts with reduced Ir-composition has been suggested as goals, but no systematic strategy to discover such catalysts has been reported. In this work, we performed high-throughput computational screening to investigate bimetalic oxide catalysts with space groups derived from those of IrO$_x$, identified promising OER catalysts predicted to satisfy all the desired properties: Co-Ir, Fe-Ir and Mo-Ir bimetallic oxides. We find that for the given crystal structures explored, it is essential to include noble metals to maintain the acid-stability, although one-to-one mixing of noble and non-noble metal oxides could keep the materials survive under the acidic conditions. Based on the calculated results, we provide insights to efficiently perform future high-throughput screening to discover catalysts with desirable properties.

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.

Kinetic Solvent Effects in Organic Reactions

2017 ◽  
Author(s):  
Belinda Slakman ◽  
Richard West
Keyword(s):  
Solvent Effect ◽  
Reaction Kinetics ◽  
Computational Methods ◽  
High Throughput ◽  
Kinetic Modeling ◽  
Solvent Effects ◽  
Reaction Rates ◽  
Prior Work ◽  
Solvent Phase ◽  
High Throughput Manner

<div> <div> <div> <p>This article reviews prior work studying reaction kinetics in solution, with the goal of using this information to improve detailed kinetic modeling in the solvent phase. Both experimental and computational methods for calculating reaction rates in liquids are reviewed. Previous studies, which used such methods to determine solvent effects, are then analyzed based on reaction family. Many of these studies correlate kinetic solvent effect with one or more solvent parameters or properties of reacting species, but it is not always possible, and investigations are usually done on too few reactions and solvents to truly generalize. From these studies, we present suggestions on how best to use data to generalize solvent effects for many different reaction types in a high throughput manner. </p> </div> </div> </div>

High-Throughput Screening to Identify Small Molecules That Selectively Inhibit APOL1 Protein Level in Podocytes

2021 ◽  
pp. 247255522110262
Author(s):  
Jonathan Choy ◽  
Yanqing Kan ◽  
Steve Cifelli ◽  
Josephine Johnson ◽  
Michelle Chen ◽  
...  
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Strategy ◽  
Time Resolved ◽  
Protein Levels ◽  
Increased Risk ◽  
Compound Screening ◽  
High Throughput Screens ◽  
Screening Library

High-throughput phenotypic screening is a key driver for the identification of novel chemical matter in drug discovery for challenging targets, especially for those with an unclear mechanism of pathology. For toxic or gain-of-function proteins, small-molecule suppressors are a targeting/therapeutic strategy that has been successfully applied. As with other high-throughput screens, the screening strategy and proper assays are critical for successfully identifying selective suppressors of the target of interest. We executed a small-molecule suppressor screen to identify compounds that specifically reduce apolipoprotein L1 (APOL1) protein levels, a genetically validated target associated with increased risk of chronic kidney disease. To enable this study, we developed homogeneous time-resolved fluorescence (HTRF) assays to measure intracellular APOL1 and apolipoprotein L2 (APOL2) protein levels and miniaturized them to 1536-well format. The APOL1 HTRF assay served as the primary assay, and the APOL2 and a commercially available p53 HTRF assay were applied as counterscreens. Cell viability was also measured with CellTiter-Glo to assess the cytotoxicity of compounds. From a 310,000-compound screening library, we identified 1490 confirmed primary hits with 12 different profiles. One hundred fifty-three hits selectively reduced APOL1 in 786-O, a renal cell adenocarcinoma cell line. Thirty-one of these selective suppressors also reduced APOL1 levels in conditionally immortalized human podocytes. The activity and specificity of seven resynthesized compounds were validated in both 786-O and podocytes.

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