The Catalyst Genome.
Jens K. Nørskov, Thomas Bligaard
Published 2013 · Chemistry, Medicine
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The quest for the materials genome— the properties of a material that define its functional properties—has started. This signifies a transition to a new era of materials research where large amounts of materials data become available. The expectation is that this will significantly speed up the discovery of new materials. This is particularly true in the area of catalytic materials, where there is an urgent need for new catalysts and processes to enable the sustainable production of fuels and chemicals.
This paper is referenced by
Identifying key descriptors in surface binding: interplay of surface anchoring and intermolecular interactions for carboxylates on Au(110)† †Electronic supplementary information (ESI) available: Supporting experimental methods and supporting discussion are included in the supplementary information.
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Magnus Mortén (2018)
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Sebastian Kozuch (2013)
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Liam Wilbraham (2018)
Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data
Keisuke Takahashi (2018)
Degree of rate control approach to computational catalyst screening
Christopher A. Wolcott (2015)
Heterogeneous Catalysis and a Sustainable Future
Jens K. Nørskov (2014)
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Kai-qi Zhang (2019)
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Jon Paul Janet (2017)
Tailoring the catalytic activity of electrodes with monolayer amounts of foreign metals.
Federico Calle-Vallejo (2013)
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Laura Pirro (2019)
An industrial perspective on the impact of Haldor Topsøe on computational chemistry
Poul Georg Moses (2015)
Coupled and Implicit Relationships of the d-Band Center of the Magnetic Dopants in Diluted Magnetic Semiconductors and Transition Metal Oxides
Antonis N. Andriotis (2017)
Lithium imide synergy with 3d transition-metal nitrides leading to unprecedented catalytic activities for ammonia decomposition.
Jianping Guo (2015)
A fast species redistribution approach to accelerate the kinetic Monte Carlo simulation for heterogeneous catalysis.
Xiao-Ming Cao (2020)
Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts
Jun Fujima (2020)
First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reaction.
Joonhee Kang (2018)
The Rise of Catalyst Informatics: Towards Catalyst Genomics
Keisuke Takahashi (2019)
Tuning oxide activity through modification of the crystal and electronic structure: from strain to potential polymorphs.
Zhongnan Xu (2015)
Modeling and Simulations in Photoelectrochemical Water Oxidation: From Single Level to Multiscale Modeling.
Xueqing Zhang (2016)
Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k
Jon Paul Janet (2017)
Theoretical Models for Bimetallic Surfaces and Nanoalloys
Hong Zhou Jiang (2018)
Perspective: Interactive material property databases through aggregation of literature data
R. K. Seshadri (2016)
Prediction of the dopant activity of chemical compounds against ammonia borane with key descriptors: electronegativity and crystal structures
Keisuke Takahashi (2016)
Catalyst screening: Refinement of the origin of the volcano curve and its implication in heterogeneous catalysis
Yu Mao (2015)
Representation of molecular structures with persistent homology for machine learning applications in chemistry
Jacob Townsend (2020)
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Raymond Gasper (2018)
A primer about Machine Learning in Catalysis ‐ A tutorial with code
Stefan Palkovits (2020)
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Christopher A. Wolcott (2014)
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