Towards the computational design of solid catalysts.

By Jens K. Nørskov, Thomas Bligaard, J. Rossmeisl, C. Christensen. 2009

3,535 citations recorded by the source corpus.

Subfields: Materials Chemistry, Renewable Energy, Sustainability and the Environment

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Stefano Curtarolo
2. Jens K. Nørskov
3. S. Qiao
4. Jeff Greeley
5. Yao Zheng
6. Y. Jiao
7. Natalio Mingo
8. Marco Buongiorno Nardelli

In Renewable Energy, Sustainability and the Environment

1. S. Qiao
2. Yao Zheng
3. Y. Jiao
4. Jens K. Nørskov
5. Jeff Greeley
6. V. Stamenković
7. J. Rossmeisl
8. N. Markovic

Citations in the corpus

• Learning atoms for materials discovery (2018, methodology): “Two atom–environment pairs are generated from Bi2Se3: For atom Bi , the environment is represented as “(2)Se3”; for atom Se , the environment is represented as “(3)Bi2.”
• CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends (2015, background): “The combination of these techniques leads to the ability to predict trends in kinetic properties of heterogeneous catalysts across a range of surfaces using a ‘‘descriptor-based’’ analysis [10, 11].”
• Machine learning for heterogeneous catalyst design and discovery (2018, methodology): “45 Other catalyst descriptors 51 derived by intuition exist such as the “generalized” coordination number 52 or “orbital-wise” coordination number, 53 which can estimate the chemical reactivity of nanoparticle catalysts by rationally counting the atoms (or their orbital overlap) that influence the…”