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Structure and stability prediction of compounds with evolutionary algorithms.

Benjamin C Revard1, William W Tipton, Richard G Hennig

  • 1Department of Materials Science and Engineering, Cornell University, Ithaca, 14853, New York, USA.

Topics in Current Chemistry
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Summary
This summary is machine-generated.

Evolutionary algorithms (EAs) are advancing crystal structure prediction by optimizing free energy calculations. This review details EA methods and applications for predicting crystal structures and compositions.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Physics

Background:

  • Crystal structure prediction is a significant challenge in the physical sciences.
  • Recent advancements frame this as a global optimization problem, utilizing accurate free energy calculations.
  • Evolutionary algorithms (EAs) have become a primary method for solving this optimization problem.

Purpose of the Study:

  • To review the current state of evolutionary algorithms for crystal structure and composition prediction.
  • To discuss methodological and algorithmic choices in EA design for this task.
  • To highlight the application of these algorithms to scientifically relevant systems.

Main Methods:

  • Framing crystal structure prediction as a global optimization problem.
  • Leveraging robust and accurate free energy calculations.
  • Employing and analyzing various evolutionary algorithms (EAs) and their design choices.

Main Results:

  • Demonstrated practical success in crystal structure prediction using EAs.
  • Detailed discussion of diverse methodological and algorithmic considerations for EA design.
  • Comprehensive review of EA applications across various scientific systems.

Conclusions:

  • Evolutionary algorithms are a powerful and evolving tool for crystal structure and composition prediction.
  • Methodological and algorithmic innovations continue to enhance EA performance.
  • These algorithms have broad applicability in both fundamental and applied scientific research.