Beyond Scaling Relations for the Description of Catalytic Materials

By M. Andersen, Sergey V. Levchenko, Matthias Scheffler, Karsten Reuter. ACS Catalysis, 2019

204 citations recorded by the source corpus.

Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed...

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

View Paper: https://www.semanticscholar.org/paper/55f02d512c05488985c5fa611bbbdd485477869c

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. Rohit Batra
2. Rampi Ramprasad
3. Le Song
4. Yuri Mishin
5. Nour Makke
6. Sanjay Chawla

Citations in the corpus

• Emerging materials intelligence ecosystems propelled by machine learning (2020, methodology): “More importantly, both LASSO and SISSO approaches have been used to rationally distil key materials features that are most correlated with target properties, providing simple and practical guidelines for experiments 86,87 .”