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

ACS Catalysis ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 7651-7659 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary W. Ulissi
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Oxygen Evolution ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Active Oxygen ◽  
Functional Theory

Accelerating the oxygen evolution reaction kinetics of Co3O4 in neutral electrolyte by decorating RuO2

2021 ◽  
Author(s):  
Kaifa Lu ◽  
Guanru Chang ◽  
Hui Zhang ◽  
Xin-Yao Yu
Keyword(s):  
Density Functional Theory ◽  
Reaction Kinetics ◽  
Oxygen Evolution ◽  
Oxygen Evolution Reaction ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Functional Theory ◽  
Neutral Electrolyte ◽  
Kinetics Of

By combining density functional theory calculations and experiments, we have demonstrated that the decoration of RuO2 can effectively accelerate the oxygen evolution reaction kinetics of Co3O4 in neutral electrolyte.

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>

Is a Non-Transition Element Nitride Ferromagnet Ever Achievable? Indication of the N-N Pairing Instability

2006 ◽  
Vol 71 (11-12) ◽  
pp. 1525-1531 ◽  
Author(s):  
Wojciech Grochala
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Transition Element ◽  
Density Functional Theory Calculations ◽  
Functional Theory

The enthalpy of four polymorphs of CaN has been scrutinized at 0 and 100 GPa using density functional theory calculations. It is shown that structures of diamagnetic calcium diazenide (Ca2N2) are preferred over the cubic ferromagnetic polymorph (CaN) postulated before, both at 0 and 100 GPa.

scholarly journals Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Chih-Chuen Lin ◽  
Phani Motamarri ◽  
Vikram Gavini
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Large Scale ◽  
Density Functional Theory Calculations ◽  
System Size ◽  
Real Space ◽  
Functional Theory ◽  
Reduced Order ◽  
Separable Approximation ◽  
Tensor Basis

AbstractWe present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.

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