Computational databases and high-throughput calculations

NIST Computational chemistry comparison and benchmark database

The Open Quantum Materials Database

Materials Project (MIT, LBNL)

aflowlib.org (Duke U)

A Jain, K A Persson, G Ceder, Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases, APL Mater 4, 053102 (2016)

S Curtarolo, G L W Hart, M B Nardelli, N Mingo, S Sanvito, O Levy, The high-throughput highway to computational materials design, Nat Mater 12, 191 (2013)

E Kim, K Huang, A Saunders, A McCallum, G Ceder, E Olivetti, Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning, Chem Mater 29, 9436 (2017)

L M Ghiringhelli, J Vybiral, E Ahmetcik, R Ouyang, S V Levchenko, C Draxl, M Scheffler, Learning physical descriptors for materials science by compressed sensing, New J Phys 19, 023017 (2017)

L Ward, A Agrawal, A Choudhary, C Wolverton, A general-purpose machine learning framework for predicting properties of inorganic materials, npj Comp Mater 2, 16028 (2016)

L M Ghiringhelli, J Vybiral, S V Levchenko, C Draxl, M Scheffler, Big Data of Materials Science: Critical Role of the Descriptor, PRL 114, 105503 (2015)