Computational high-throughput screening of electrocatalytic materials for hydrogen evolution

By Jeff Greeley, T. Jaramillo, J. Bonde, I. Chorkendorff, Jens K. Nørskov. Nature Materials, 2006

3,533 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/08de33def17d37ffc1efa6286c962912a83abf3b

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. M. Chhowalla
3. Natalio Mingo
4. Jesús Carrete
5. S. Qiao
6. Dane Morgan
7. Ryan Jacobs
8. Shidong Wang

In Mechanical Engineering

1. R. B. Grubbs
2. Jingguang G. Chen
3. S. Qiao
4. M. Jaroniec
5. Yang Shao-Horn
6. Thomas Bligaard
7. Yao Zheng
8. Lain‐Jong Li

In Renewable Energy, Sustainability and the Environment

1. S. Qiao
2. Yao Zheng
3. Y. Jiao
4. M. Jaroniec
5. Xuping Sun
6. Abdullah M. Asiri
7. Jens K. Nørskov
8. Q. Jiang

Citations in the corpus

• Machine Learning Strategy for Accelerated Design of Polymer Dielectrics (2016, background): “The traditional routes adopted thus far to meet such design goals involve the determination of the relevant properties of a large number of potential candidates, via high-throughput experiments or computations, and choosing the best cases for further studies and optimisation 1–4 .”