New cubic perovskites for one- and two-photon water splitting using the computational materials repository

By I. Castelli, D. D. Landis, Kristian Sommer Thygesen, S. Dahl, I. Chorkendorff, T. Jaramillo, Karsten Wedel Jacobsen. 2012

288 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Inorganic Chemistry, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/c0df64a8e3d9403368e15480e95c57d91989f5de

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Ryan Jacobs
2. Dane Morgan
3. Anubhav Jain
4. Alex M. Ganose
5. Gerbrand Ceder

In Inorganic Chemistry

1. Wei Li
2. Anubhav Jain
3. Ryan Jacobs
4. Dane Morgan

In Renewable Energy, Sustainability and the Environment

1. Ryan Jacobs
2. Dane Morgan
3. Wei Li
4. Anubhav Jain

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”
• Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm (2020, background): “While there are commonly used datasets for materials problems, e.g., Castelli et al.’s investigation of cubic perovskites9, it is uncommon for two algorithms to be tested against the same dataset and with the same data cleaning procedures.”