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A new stochastic model for nucleation reveals classical nucleation theory (CNT) significantly underestimates critical cluster formation time due to complex dynamics, not free energy errors. This finding impacts understanding protein crystallization and phase transitions.

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Area of Science:

  • Physical Chemistry
  • Biophysics
  • Materials Science

Background:

  • Classical nucleation theory (CNT) is the standard model for nucleation phenomena.
  • Understanding nucleation is crucial for processes like protein crystallization and phase transitions.
  • Existing models may not fully capture the complex dynamics involved in nucleation.

Purpose of the Study:

  • To develop a more accurate stochastic model for diffusion-limited nucleation.
  • To generalize the standard classical nucleation theory (CNT) by incorporating additional variables.
  • To investigate the discrepancy between theoretical predictions and simulation results for nucleation.

Main Methods:

  • Developed a two-variable stochastic model based on fluctuating hydrodynamics.
  • Employed the weak-noise approximation to calculate nucleation rate and pathway.
  • Validated the model against direct numerical simulations of globular protein transitions.

Main Results:

  • The developed model shows good agreement with numerical simulations.
  • Classical nucleation theory (CNT) underestimates critical cluster formation time by two orders of magnitude.
  • The discrepancy is attributed to the complex dynamics of the two-variable model, not free energy barrier estimation.

Conclusions:

  • The two-variable stochastic model provides a more accurate description of diffusion-limited nucleation.
  • CNT's limitations in predicting nucleation timescales are significant, especially for complex systems.
  • This work refines our understanding of nucleation dynamics in protein crystallization and related phenomena.