Literature DB >> 20218599

Uncovering compounds by synergy of cluster expansion and high-throughput methods.

Ohad Levy1, Gus L W Hart, Stefano Curtarolo.   

Abstract

Predicting from first-principles calculations whether mixed metallic elements phase-separate or form ordered structures is a major challenge of current materials research. It can be partially addressed in cases where experiments suggest the underlying lattice is conserved, using cluster expansion (CE) and a variety of exhaustive evaluation or genetic search algorithms. Evolutionary algorithms have been recently introduced to search for stable off-lattice structures at fixed mixture compositions. The general off-lattice problem is still unsolved. We present an integrated approach of CE and high-throughput ab initio calculations (HT) applicable to the full range of compositions in binary systems where the constituent elements or the intermediate ordered structures have different lattice types. The HT method replaces the search algorithms by direct calculation of a moderate number of naturally occurring prototypes representing all crystal systems and guides CE calculations of derivative structures. This synergy achieves the precision of the CE and the guiding strengths of the HT. Its application to poorly characterized binary Hf systems, believed to be phase-separating, defines three classes of alloys where CE and HT complement each other to uncover new ordered structures.

Entities:  

Year:  2010        PMID: 20218599     DOI: 10.1021/ja9105623

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  5 in total

1.  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

2.  First principles search for n-type oxide, nitride, and sulfide thermoelectrics.

Authors:  Kevin F Garrity
Journal:  Phys Rev B       Date:  2016-07-15       Impact factor: 4.036

3.  Accelerating materials property predictions using machine learning.

Authors:  Ghanshyam Pilania; Chenchen Wang; Xun Jiang; Sanguthevar Rajasekaran; Ramamurthy Ramprasad
Journal:  Sci Rep       Date:  2013-09-30       Impact factor: 4.379

4.  Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases.

Authors:  Eric Perim; Dongwoo Lee; Yanhui Liu; Cormac Toher; Pan Gong; Yanglin Li; W Neal Simmons; Ohad Levy; Joost J Vlassak; Jan Schroers; Stefano Curtarolo
Journal:  Nat Commun       Date:  2016-08-02       Impact factor: 14.919

5.  Universal fragment descriptors for predicting properties of inorganic crystals.

Authors:  Olexandr Isayev; Corey Oses; Cormac Toher; Eric Gossett; Stefano Curtarolo; Alexander Tropsha
Journal:  Nat Commun       Date:  2017-06-05       Impact factor: 14.919

  5 in total

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