Temperature scaling method for Markov chains.

Area of Science:

• Computational Chemistry
• Physical Chemistry
• Chemical Physics

Background:

• Ab initio potentials in Monte Carlo simulations for nucleation kinetics are computationally expensive.
• Investigating temperature dependence of kinetic properties necessitates simulations at multiple temperatures, increasing computational cost.

Purpose of the Study:

• To introduce and validate a Markov chain temperature-scaling (TeS) method.
• To reduce the computational expense of simulations investigating temperature-dependent kinetic properties.
• To demonstrate the general applicability of TeS for various simulation applications.

Main Methods:

• Developed a Markov chain temperature-scaling (TeS) method.
• Applied TeS to a 1-D analytical potential with known exact solutions.
• Utilized TeS with the Dang-Chang polarizable classical potential for water clusters (2-5 monomers).
• Employed Dynamical Nucleation Theory to determine monomer loss evaporation rate constants.

Main Results:

• Demonstrated that Markov chains from one temperature can be scaled to others without additional simulations.
• Validated the TeS method using a 1-D analytical potential.
• Obtained statistical properties for water clusters at various temperatures using scaled Markov chains.
• Determined evaporation rate constants for water clusters.

Conclusions:

• The TeS method significantly reduces computational cost for temperature-dependent simulations.
• TeS provides accurate results comparable to direct simulations.
• This method is broadly applicable to various simulation studies requiring temperature variations.