High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications

By A. Emery, James E. Saal, S. Kirklin, Vinay I. Hegde, Chris Wolverton. 2016

219 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5ba99a3e170fa25d23c8bf33d514063f63223a71

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. Ryan Jacobs
2. Dane Morgan
3. Rohit Batra
4. Rampi Ramprasad
5. Chiho Kim
6. A. Mannodi‐Kanakkithodi
7. Ghanshyam Pilania
8. Wei Li

In Renewable Energy, Sustainability and the Environment

1. Ryan Jacobs
2. Dane Morgan
3. Wei Li

Citations in the corpus

• Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, methodology): “Recently, high-throughput DFT methods have been used to guide the discovery of new compounds for numerous applications, including: perovskite oxides for solid oxide fuel cell (SOFC) cathodes[1, 2], thermochemical water splitting,[3] half-heusler and sintered compounds for thermoelectrics,[4, 5] oxid”
• Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, background): “Efforts are also underway for the identifi-cation of novel shape memory alloys [46], improved piezoelectrics [47], MAX phases [47,48], novel perovskite [49] and double perovskite halides [32, 49], CO 2 capture materials [50] and potential candidates for water splitting [51].”