AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

By Stefano Curtarolo, W. Setyawan, Shidong Wang, Junkai Xue, Kesong Yang, Richard H. Taylor, Gus L. W. Hart, Stefano Sanvito, Marco Buongiorno Nardelli, Natalio Mingo and 1 more. 2012

1,034 citations recorded by the source corpus.

Subfields: Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/00d85607f350cee1840af5765ab022f1b9527657

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In Materials Chemistry

1. Stefano Curtarolo
2. Natalio Mingo
3. Cormac Toher
4. Marco Buongiorno Nardelli
5. Sam Schoenholz
6. Ekin Dogus Cubuk
7. Zeliang Liu
8. Shyue Ping Ong

Citations in the corpus

• Criteria for Predicting the Formation of Single-Phase High-Entropy Alloys (2015, background): “and the alloy database of Widom and co-workers [28,32].”
• Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation (2020, methodology): “…to predict material properties [4,7 – 18], using the large-scale data collection based on computation by Density Functional Theory (DFT)[19], such as the Materials Project (MP)[20], Open Quantum Materials Database (OQMD)[21,22], the Automatic Flow of Materials Discovery Library (AFLOWLIB.ORG)[23].”