Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Dataset’s chemical diversity limits the generalizability of machine learning predictions

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta Glavatskikh ◽  
Jules Leguy ◽  
Gilles Hunault ◽  
Thomas Cauchy ◽  
Benoit Da Mota
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Chemical Space ◽  
Chemical Properties ◽  
Density Functional Theory Calculations ◽  
Real Data ◽  
Chemical Diversity ◽  
Energy Prediction ◽  
The Neural Network ◽  
Golden Standard

Abstract The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations. Such ML models need to be tested and generalized on real data. PC9, a new QM9 equivalent dataset (only H, C, N, O and F and up to 9 “heavy” atoms) of the PubChemQC project is presented in this article. A statistical study of bonding distances and chemical functions shows that this new dataset encompasses more chemical diversity. Kernel Ridge Regression, Elastic Net and the Neural Network model provided by SchNet have been used on both datasets. The overall accuracy in energy prediction is higher for the QM9 subset. However, a model trained on PC9 shows a stronger ability to predict energies of the other dataset.

scholarly journals A Theory-guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition Metal Catalysts

2021 ◽  
Author(s):  
Yizhen Chen ◽  
Rachita Rana ◽  
Tyler Sours ◽  
FERNANDO D. VILA ◽  
Shaohong Cao ◽  
...  
Keyword(s):  
Active Sites ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Metal Catalysts ◽  
Transition Metal Catalysts ◽  
Supported Metal Catalysts ◽  
Exafs Analysis ◽  
Catalytically Active ◽  
Metal Support ◽  
Resolution Imaging

Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically-dispersed Pt supported on crystalline MgO (chosen for its well-defined bonding sites for Pt) as a prototypical example, in this work, we demonstrate how systematic density functional theory calculations (for assessing all the potentially stable Pt sites) combined with automated EXAFS analysis can lead to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging, EXAFS, and HERFD-XANES spectroscopies; the proposed Pt site are in full agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active sites than what is currently possible with conventional EXAFS analysis. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities.

Field Effect Modulation of Electrocatalytic Hydrogen Evolution at Back-Gated Two-Dimensional MoS2 Electrodes

2019 ◽  
Author(s):  
Yan Wang ◽  
Sagar Udyavara ◽  
Matthew Neurock ◽  
C. Daniel Frisbie
Keyword(s):  
Electric Field ◽  
Hydrogen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Electrode Materials ◽  
Density Functional Theory Calculations ◽  
Electronic Charge ◽  
Back Side ◽  
Gate Electrode ◽  
Conventional Manner

<div> <div> <div> <p> </p><div> <div> <div> <p>Electrocatalytic activity for hydrogen evolution at monolayer MoS2 electrodes can be enhanced by the application of an electric field normal to the electrode plane. The electric field is produced by a gate electrode lying underneath the MoS2 and separated from it by a dielectric. Application of a voltage to the back-side gate electrode while sweeping the MoS2 electrochemical potential in a conventional manner in 0.5 M H2SO4 results in up to a 140-mV reduction in overpotential for hydrogen evolution at current densities of 50 mA/cm2. Tafel analysis indicates that the exchange current density is correspondingly improved by a factor of 4 to 0.1 mA/cm2 as gate voltage is increased. Density functional theory calculations support a mechanism in which the higher hydrogen evolution activity is caused by gate-induced electronic charge on Mo metal centers adjacent the S vacancies (the active sites), leading to enhanced Mo-H bond strengths. Overall, our findings indicate that the back-gated working electrode architecture is a convenient and versatile platform for investigating the connection between tunable electronic charge at active sites and overpotential for electrocatalytic processes on ultrathin electrode materials.</p></div></div></div><br><p></p></div></div></div>

scholarly journals Study on the adsorption selection of CH$$_4$$ on CuO (110) versus (111) surfaces: a density functional theory study

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Long Lin ◽  
Linwei Yao ◽  
Shaofei Li ◽  
Zhengguang Shi ◽  
Kun Xie ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Adsorption Capacity ◽  
Active Sites ◽  
Density Functional ◽  
Oxygen Vacancies ◽  
Density Functional Theory Calculations ◽  
Density Functional Theory Study ◽  
Functional Theory ◽  
Strong Adsorption ◽  
Selection Of

AbstractFinding the active sites of suitable metal oxides is a key prerequisite for detecting CH$$_4$$ 4 . The purpose of the paper is to investigate the adsorption of CH$$_4$$ 4 on intrinsic and oxygen-vacancies CuO (111) and (110) surfaces using density functional theory calculations. The results show that CH$$_4$$ 4 has a strong adsorption energy of −0.370 to 0.391 eV at all site on the CuO (110) surface. The adsorption capacity of CH$$_4$$ 4 on CuO (111) surface is weak, ranging from −0.156 to −0.325 eV. In the surface containing oxygen vacancies, the adsorption capacity of CuO surface to CH$$_4$$ 4 is significantly stronger than that of intrinsic CuO surface. The results indicate that CuO (110) has strong adsorption and charge transfer capacity for CH$$_4$$ 4 , which may provide experimental guidance.

scholarly journals The Effect of Iron Impurities on Transition Metal Catalysts for the Oxygen Evolution Reaction in Alkaline Environment: Activity Mediators or Active Sites?

2020 ◽  
Author(s):  
Ioannis Spanos ◽  
Justus Masa ◽  
Aleksandar Zeradjanin ◽  
Robert Schlögl
Keyword(s):  
Transition Metal ◽  
Oxygen Evolution ◽  
Oxygen Evolution Reaction ◽  
Active Sites ◽  
Water Electrolysis ◽  
Metal Catalysts ◽  
Transition Metal Catalysts ◽  
Alkaline Water Electrolysis ◽  
Co Catalysts ◽  
Model Transition

AbstractThere is an ongoing debate on elucidating the actual role of Fe impurities in alkaline water electrolysis, acting either as reactivity mediators or as co-catalysts through synergistic interaction with the main catalyst material. This perspective summarizes the most prominent oxygen evolution reaction (OER) mechanisms mostly for Ni-based oxides as model transition metal catalysts and highlights the effect of Fe incorporation on the catalyst surface in the form of impurities originating from the electrolyte or co-precipitated in the catalyst lattice, in modulating the OER reaction kinetics, mechanism and stability. Graphic Abstract

scholarly journals Mechanism of selective benzene hydroxylation catalyzed by iron-containing zeolites

2018 ◽  
Vol 115 (48) ◽  
pp. 12124-12129 ◽  
Author(s):  
Benjamin E. R. Snyder ◽  
Max L. Bols ◽  
Hannah M. Rhoda ◽  
Pieter Vanelderen ◽  
Lars H. Böttger ◽  
...  
Keyword(s):  
High Activity ◽  
Active Site ◽  
Active Sites ◽  
High Performance ◽  
Density Functional ◽  
Catalyst Deactivation ◽  
Density Functional Theory Calculations ◽  
Oxidation Catalysis ◽  
Spectroscopic Techniques ◽  
Benzene Hydroxylation

A direct, catalytic conversion of benzene to phenol would have wide-reaching economic impacts. Fe zeolites exhibit a remarkable combination of high activity and selectivity in this conversion, leading to their past implementation at the pilot plant level. There were, however, issues related to catalyst deactivation for this process. Mechanistic insight could resolve these issues, and also provide a blueprint for achieving high performance in selective oxidation catalysis. Recently, we demonstrated that the active site of selective hydrocarbon oxidation in Fe zeolites, named α-O, is an unusually reactive Fe(IV)=O species. Here, we apply advanced spectroscopic techniques to determine that the reaction of this Fe(IV)=O intermediate with benzene in fact regenerates the reduced Fe(II) active site, enabling catalytic turnover. At the same time, a small fraction of Fe(III)-phenolate poisoned active sites form, defining a mechanism for catalyst deactivation. Density-functional theory calculations provide further insight into the experimentally defined mechanism. The extreme reactivity of α-O significantly tunes down (eliminates) the rate-limiting barrier for aromatic hydroxylation, leading to a diffusion-limited reaction coordinate. This favors hydroxylation of the rapidly diffusing benzene substrate over the slowly diffusing (but more reactive) oxygenated product, thereby enhancing selectivity. This defines a mechanism to simultaneously attain high activity (conversion) and selectivity, enabling the efficient oxidative upgrading of inert hydrocarbon substrates.

First-Principles DFT Insights into the Mechanisms of CO2 Reduction to CO on Fe (100)-Ni Bimetals

2021 ◽  
Author(s):  
Caroline Kwawu ◽  
Albert Aniagyei ◽  
Destiny Konadu ◽  
Elliot Menkah ◽  
Richard Tia
Keyword(s):  
Carbon Monoxide ◽  
Binding Site ◽  
Active Sites ◽  
Density Functional ◽  
Co2 Reduction ◽  
Density Functional Theory Calculations ◽  
Hydrocarbon Fuel ◽  
Generalized Gradient ◽  
Efficient Catalyst ◽  
Stepped Surface

Abstract Iron and nickel are known active sites in the enzyme carbon monoxide dehydrogenases (CODH) which catalyzes CO2 to CO reversibly. The presence of nickel impurities in the earth abundant iron surface could provide a more efficient catalyst for CO2 degradation into CO, which is a feedstock for hydrocarbon fuel production. In the present study, we have employed spin-polarized dispersion-corrected density functional theory calculations within the generalized gradient approximation to elucidate the active sites on Fe (100)-Ni bimetals. We sort to ascertain the mechanism of CO2 dissociation to carbon monoxide on Ni deposited and alloyed surfaces at 0.25, 0.50 and 1 monolayer (ML) impurity concentrations. CO2 and (CO + O) bind exothermically i.e., -0.87 eV and − 1.51 eV respectively to the bare Fe (100) surface with a decomposition barrier of 0.53 eV. The presence of nickel generally lowers the amount of charge transferred to CO2 moiety. Generally, the binding strengths of CO2 were reduced on the modified surfaces and the extent of its activation was lowered. The barriers for CO2 dissociation increased mainly upon introduction of Ni impurities which is undesired. However, the 0.5 ML deposited (FeNi0.5(A)) surface is promising for CO2 decomposition, providing a lower energy barrier (of 0.32 eV) than the pristine Fe (100) surface. This active 1-dimensional defective FeNi0.5(A) surface provides a stepped surface and Ni-Ni bridge binding site for CO2 on Fe (100). Ni-Ni bridge site on Fe (100) is more effective for both CO2 binding or sequestration and dissociation compared to the stepped surface providing the Fe-Ni bridge binding site.

scholarly journals Molecular Rh(III) and Ir(III) Catalysts Immobilized on Bipyridine-Based Covalent Triazine Frameworks for the Hydrogenation of CO2 to Formate

Catalysts ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 295 ◽  
Author(s):  
Gunniya Gunasekar ◽  
Kwangho Park ◽  
Hyeonseok Jeong ◽  
Kwang-Deog Jung ◽  
Kiyoung Park ◽  
...  
Keyword(s):  
Density Functional ◽  
Three Dimensional ◽  
Catalytic Efficiency ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Two Dimensional ◽  
Functional Theory ◽  
Hydrogenation Of Co2 ◽  
Catalytic Reactivity ◽  
Reduced Activity

The catalytic reactivity of molecular Rh(III)/Ir(III) catalysts immobilized on two- and three-dimensional Bipyridine-based Covalent Triazine Frameworks (bpy-CTF) for the hydrogenation of CO2 to formate has been described. The heterogenized Ir complex demonstrated superior catalytic efficiency over its Rh counterpart. The Ir catalyst immobilized on two-dimensional bpy-CTF showed an improved turnover frequency and turnover number compared to its three-dimensional counterpart. The two-dimensional Ir catalyst produced a maximum formate concentration of 1.8 M and maintained its catalytic efficiency over five consecutive runs with an average of 92% in each cycle. The reduced activity after recycling was studied by density functional theory calculations, and a plausible leaching pathway along with a rational catalyst design guidance have been proposed.

Sign in / Sign up

Export Citation Format

Share Document