← Back to Search
Identifying Systematic DFT Errors In Catalytic Reactions
R. Christensen, H. Hansen, T. Vegge
Published 2015 · Chemistry
Download PDFAnalyze on Scholarcy
Using CO2 reduction reactions as examples, we present a widely applicable method for identifying the main source of errors in density functional theory (DFT) calculations. The method has broad applications for error correction in DFT calculations in general, as it relies on the dependence of the applied exchange–correlation functional on the reaction energies rather than on errors versus the experimental data. As a result, improved energy corrections can now be determined for both gas phase and adsorbed reaction species, particularly interesting within heterogeneous catalysis. We show that for the CO2 reduction reactions, the main source of error is associated with the CO bonds and not the typically energy corrected OCO backbone.
This paper references
Higher-accuracy van der Waals density functional
Kyuho Lee (2010)
First-principles kinetic modeling in heterogeneous catalysis: an industrial perspective on best-practice, gaps and needs
M. Sabbe (2012)
Generalized Gradient Approximation Made Simple.
How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels
A. A. Peterson (2010)
A benchmark database for adsorption bond energies to transition metal surfaces and comparison to selected DFT functionals
J. Wellendorff (2015)
A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels.
J. Qiao (2014)
The Active Site of Methanol Synthesis over Cu/ZnO/Al2O3 Industrial Catalysts
M. Behrens (2012)
Activity Descriptors for CO2 Electroreduction to Methane on Transition-Metal Catalysts
A. A. Peterson (2012)
Theoretical considerations on the electroreduction of CO to C2 species on Cu(100) electrodes.
F. Calle-Vallejo (2013)
Modern Aspects of Electrochemistry: No. 6
J. Bockris (1968)
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.
Choosing a proper exchange-correlation functional for the computational catalysis on surface.
B. Teng (2014)
Electrochemical CO 2 Reduction on Metal Electrodes
Y. Hori (2008)
Van der Waals density functionals applied to solids
Jivr'i Klimevs (2011)
From ultrasoft pseudopotentials to the projector augmented-wave method
G. Kresse (1999)
van der Waals density functional for general geometries.
M. Dion (2004)
Projector augmented-wave method.
Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals
B. Hammer (1999)
An object-oriented scripting interface to a legacy electronic structure code
S. R. Bahn (2002)
Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation
J. Wellendorff (2012)
Modern Aspects of Electrochemistry
B. E. Conway (1971)
A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO₂ reduction.
S. Lysgaard (2015)
CO and CO2 Hydrogenation to Methanol Calculated Using the BEEF-vdW Functional
Felix Studt (2012)
Heterogeneous catalytic conversion of CO2: a comprehensive theoretical review.
Ya-wei Li (2015)
The Mechanism of CO and CO2 Hydrogenation to Methanol over Cu‐Based Catalysts
Felix Studt (2015)
This paper is referenced by
The role of uncertainty quantification and propagation in accelerating the discovery of electrochemical functional materials
Gregory Houchins (2019)
Investigating methane dry reforming on Ni and B promoted Ni surfaces: DFT assisted microkinetic analysis and addressing the coking problem
Ojus Mohan (2020)
Investigation on the effect of an anion layer on photocatalytic activity: carbonate vs. oxalate
Weiyi Hao (2017)
Single site porphyrine-like structures advantages over metals for selective electrochemical CO 2 reduction
A. Bagger (2017)
Active learning with non-ab initio input features toward efficient CO2 reduction catalysts† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc03422a
Juhwan Noh (2018)
Intermetallic PdIn catalyst for CO2 hydrogenation to methanol: mechanistic studies with a combined DFT and microkinetic modeling method
Panpan Wu (2019)
Uncertainty quantification in first-principles predictions of phonon properties and lattice thermal conductivity
Holden L. Parks (2020)
Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides
G. Houchins (2020)
Predicting CO2 adsorption and reactivity on transition metal surfaces using popular density functional theory methods
O. Mohan (2019)
Robust high-fidelity DFT study of the lithium-graphite phase diagram
Vikram Pande (2016)
Functional Independent Scaling Relation for ORR/OER Catalysts
R. Christensen (2016)
Machine learning approaches for the prediction of materials properties
Siwar Chibani (2020)
First-principles-based multiscale modelling of heterogeneous catalysis
Albert Bruix (2019)
Higher alcohol synthesis from syngas over xerogel-derived Co-Cu-Al2O3 catalyst with an enhanced metal proximity
Seung Ju Han (2019)
Hydrodeoxygenation of Phenol to Benzene and Cyclohexane on Rh(111) and Rh(211) Surfaces: Insights from Density Functional Theory
Delfina Garcia-Pintos (2016)
Dry reforming of methane over the cobalt catalyst: Theoretical insights into the reaction kinetics and mechanism for catalyst deactivation
S. Chen (2020)
Significance of Surface Formate Coverage on the Reaction Kinetics of Methanol Synthesis from CO2 Hydrogenation over Cu
Panpan Wu (2017)
Computational insights into the strain effect on the electrocatalytic reduction of CO2 to CO on Pd surfaces.
H. Liu (2020)
Origin of CO2 as the main carbon source in syngas-to-methanol process over Cu: theoretical evidence from a combined DFT and microkinetic modeling study
D. Xu (2020)
Recent Progress in the Theoretical Investigation of Electrocatalytic Reduction of CO2
Ziqi Tian (2018)
Role of CO* as a Spectator in CO2 Electroreduction on RuO2
A. Bhowmik (2017)
Improving the Activity of M-N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption.
Katrine L. Svane (2019)
CO2 electroreduction performance of a single transition metal atom supported on porphyrin-like graphene: a computational study.
Zhongxu Wang (2017)
Identification of Pt-based catalysts for propane dehydrogenation via a probability analysis† †Electronic supplementary information (ESI) available: Calculation details. See DOI: 10.1039/c8sc00802g
Shenjun Zha (2018)
Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods and Artificial Intelligence.
Alán Aspuru-Guzik (2018)
Computational investigation of CO2 electroreduction on tin oxide and predictions of Ti, V, Nb and Zr dopants for improved catalysis
K. Saravanan (2017)
Descriptors and Thermodynamic Limitations of Electrocatalytic Carbon Dioxide Reduction on Rutile Oxide Surfaces.
A. Bhowmik (2016)
Fundamental Atomic Insight in Electrocatalysis
Alexander Bagger (2018)
pH effects on the electrochemical reduction of CO(2) towards C2 products on stepped copper
Xinyan Liu (2019)
Machine learning for molecular and materials science
K. Butler (2018)
Effect of mixed anion layer on energy band, charge separation and photochemical properties of (BiO)2OHCl
Yifei Zhai (2018)
The atomic simulation environment-a Python library for working with atoms.
Ask Hjorth Larsen (2017)See more