Materials discovery via CALYPSO methodology

Yanchao Wang, Jian Lv, Li Zhu, Shaohua Lu, Ketao Yin, Quan Li, Hui Wang, Lijun Zhang, Yanming Ma

Research output: Contribution to journalReview articlepeer-review

147 Scopus citations

Abstract

The structure prediction at the atomic level is emerging as a state-of-the-art approach to accelerate the functionality-driven discovery of materials. By combining the global swarm optimization algorithm with first-principles thermodynamic calculations, it exploits the power of current supercomputer architectures to robustly predict the ground state and metastable structures of materials with only the given knowledge of chemical composition. In this Review, we provide an overview of the basic theory and main features of our as-developed CALYPSO structure prediction method, as well as its versatile applications to design of a broad range of materials including those of three-dimensional bulks, two-dimensional reconstructed surfaces and layers, and isolated clusters/nanoparticles or molecules with a variety of functional properties. The current challenges faced by structure prediction for materials discovery and future developments of CALYPSO to overcome them are also discussed.

Original languageEnglish (US)
Article number203203
JournalJournal of Physics Condensed Matter
Volume27
Issue number20
DOIs
StatePublished - May 27 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Condensed Matter Physics

Keywords

  • CALYPSO
  • materials discovery
  • structure prediction

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