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Order-parameter-aided temperature-accelerated sampling for the exploration of crystal polymorphism and solid-liquid

Tang-Qing Yu1, Pei-Yang Chen2, Ming Chen2

  • 1Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.

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|June 9, 2014
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Summary

This study introduces a new computational method for predicting crystal polymorphism by enhancing free energy calculations. The approach successfully identified new crystal structures in xenon and modeled phase transitions in copper.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Predicting crystal polymorphism is a significant challenge in materials science and theoretical chemistry.
  • Rough energy landscapes in crystals complicate theoretical prediction of different polymorphs.
  • Free energy-based enhanced sampling methods offer a promising avenue for tackling polymorphism.

Purpose of the Study:

  • To expand upon existing enhanced sampling techniques for predicting crystal polymorphism.
  • To incorporate general order parameters and supercell variables into free energy calculations.
  • To develop an efficient strategy for reconstructing high-dimensional free energy surfaces.

Main Methods:

  • Utilized temperature-accelerated adiabatic free energy dynamics.
  • Expanded the framework to include general order parameters as collective variables.
  • Applied Steinhardt order parameters with and without supercell variables for enhanced sampling.
  • Developed a strategy for free energy surface reconstruction.

Main Results:

  • Successfully predicted polymorphism in xenon crystals at high pressure and temperature.
  • Identified new crystal structures, including fcc states with hcp stacking faults, by including supercell parameters.
  • Modeled the solid-liquid phase transition in copper at 1300 K, enabling repeated melting and refreezing.
  • Obtained free energy profiles with high efficiency.

Conclusions:

  • The enhanced sampling method effectively predicts crystal polymorphism and phase transitions.
  • Incorporating supercell parameters significantly expands the discovery of novel crystalline structures.
  • The method provides an efficient and robust approach for free energy calculations in materials science.