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Collective Variables for Crystallization Simulations-from Early Developments to Recent Advances.

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Enhanced sampling simulations accelerate the study of crystallization by using collective variables (CVs) to overcome limitations in molecular dynamics. This review categorizes CVs and discusses future directions for complex systems.

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Area of Science:

  • Physical Chemistry
  • Materials Science
  • Computational Biology

Background:

  • Crystallization is a critical physicochemical process with broad applications, yet understanding its early stages remains challenging.
  • Experimental methods reveal crystal structures but not the molecular aggregation leading to nucleation.
  • Molecular dynamics (MD) simulations offer microscopic insights but struggle with the long timescales of crystallization, a rare event.

Purpose of the Study:

  • To review collective variables (CVs) developed for enhanced sampling (ES) simulations of crystallization.
  • To categorize existing CVs and discuss their evolution and application in studying crystallization from melt or solution.
  • To identify current challenges and propose future directions for developing effective CVs for complex systems.

Main Methods:

  • Utilizes enhanced sampling (ES) simulations, a computational technique that overcomes the timescale limitations of standard molecular dynamics (MD).
  • Employs collective variables (CVs) to enhance the sampling of slow degrees of freedom by applying bias potentials.
  • Categorizes CVs into four main types: spherical particle-based, molecular template-based, physical property-based, and those from dimensionality reduction.

Main Results:

  • A comprehensive overview of various CVs used in ES simulations for studying crystallization is presented.
  • CVs are classified into four distinct categories, highlighting the diversity of approaches.
  • The review discusses the evolution of CVs in the context of crystallization studies.

Conclusions:

  • Effective CVs are crucial for accelerating the study of crystallization using enhanced sampling simulations.
  • The development of novel and robust CVs is essential for tackling the crystallization of increasingly complex systems.
  • Future research should focus on advancing CV methodologies to better capture the intricacies of crystallization processes.