Improving Efficiency in SMD Simulations Through a Hybrid Differential Relaxation Algorithm.

Area of Science:

• Computational Chemistry
• Biophysical Chemistry
• Biochemistry

Background:

• Free energy profiles are crucial for understanding biochemical reactions from simulations.
• Accurate simulations using hybrid quantum-classical methods are computationally expensive.
• Faster equilibration of the classical environment is needed for efficient simulations.

Purpose of the Study:

• To introduce and validate a novel algorithm, hybrid differential relaxation (HyDRA).
• To demonstrate HyDRA's ability to reduce computational cost in simulating biochemical reactions.
• To enable more accessible and efficient theoretical studies of enzymatic processes.

Main Methods:

• Development and testing of the hybrid differential relaxation (HyDRA) algorithm.
• Utilizing nonequilibrium steering and Jarzynski's Relationship for free energy calculations.
• Comparing HyDRA's efficiency against traditional simulation methods.

Main Results:

• HyDRA significantly accelerates the equilibration of the classical environment.
• The algorithm allows for accurate free energy profiles with fewer trajectories or faster pulling speeds.
• Reduced computational cost is achieved without compromising accuracy.

Conclusions:

• HyDRA offers a computationally efficient approach to simulate biochemical reactions.
• The method facilitates faster and more accurate determination of free energy profiles.
• HyDRA is expected to promote theoretical investigations of enzymatic mechanisms.