Literature DB >> 14525315

Predicting crystal structures with data mining of quantum calculations.

Stefano Curtarolo1, Dane Morgan, Kristin Persson, John Rodgers, Gerbrand Ceder.   

Abstract

Predicting and characterizing the crystal structure of materials is a key problem in materials research and development. It is typically addressed with highly accurate quantum mechanical computations on a small set of candidate structures, or with empirical rules that have been extracted from a large amount of experimental information, but have limited predictive power. In this Letter, we transfer the concept of heuristic rule extraction to a large library of ab initio calculated information, and we demonstrate that this can be developed into a tool for crystal structure prediction.

Year:  2003        PMID: 14525315     DOI: 10.1103/PhysRevLett.91.135503

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  21 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

2.  Topology-Based Machine Learning Strategy for Cluster Structure Prediction.

Authors:  Xin Chen; Dong Chen; Mouyi Weng; Yi Jiang; Guo-Wei Wei; Feng Pan
Journal:  J Phys Chem Lett       Date:  2020-05-21       Impact factor: 6.475

Review 3.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

4.  Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

Authors:  Rama K Vasudevan; Kamal Choudhary; Apurva Mehta; Ryan Smith; Gilad Kusne; Francesca Tavazza; Lukas Vlcek; Maxim Ziatdinov; Sergei V Kalinin; Jason Hattrick-Simpers
Journal:  MRS Commun       Date:  2019       Impact factor: 2.566

5.  Density functional theory in surface chemistry and catalysis.

Authors:  Jens K Nørskov; Frank Abild-Pedersen; Felix Studt; Thomas Bligaard
Journal:  Proc Natl Acad Sci U S A       Date:  2011-01-10       Impact factor: 11.205

Review 6.  Towards the computational design of solid catalysts.

Authors:  J K Nørskov; T Bligaard; J Rossmeisl; C H Christensen
Journal:  Nat Chem       Date:  2009-04       Impact factor: 24.427

7.  Computational materials science: Substitution with vision.

Authors:  Gus L W Hart
Journal:  Nature       Date:  2012-11-21       Impact factor: 49.962

8.  The high-throughput highway to computational materials design.

Authors:  Stefano Curtarolo; Gus L W Hart; Marco Buongiorno Nardelli; Natalio Mingo; Stefano Sanvito; Ohad Levy
Journal:  Nat Mater       Date:  2013-03       Impact factor: 43.841

9.  Can artificial intelligence create the next wonder material?

Authors:  Nicola Nosengo; Gerbrand Ceder
Journal:  Nature       Date:  2016-05-05       Impact factor: 49.962

10.  How can Databases assist with the Prediction of Chemical Compounds?

Authors:  J Christian Schön
Journal:  Z Anorg Allg Chem       Date:  2014-09-24       Impact factor: 1.492

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