Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy...

Area of Science:

• Computational chemistry
• Statistical mechanics
• Molecular dynamics simulations

Background:

• Enveloping distribution sampling (EDS) is an efficient method for calculating multiple free-energy differences.
• Constructing an effective reference-state Hamiltonian for EDS is challenging.
• Existing EDS methods may not efficiently sample all relevant states.

Purpose of the Study:

• To develop a novel approach for constructing the EDS reference-state Hamiltonian.
• To preserve local energy minima of end-states within the EDS framework.
• To introduce an intuitive and robust parameter optimization scheme for EDS.

Main Methods:

• Proposed a novel reference-state Hamiltonian construction for EDS, building on energy landscape smoothing techniques.
• Ensured the reference-state Hamiltonian preserves local energy minima of combined end-states.
• Developed an intuitive, robust, and efficient parameter optimization scheme for EDS Hamiltonian parameters.

Main Results:

• Demonstrated the proposed EDS method on established and novel test systems.
• Showcased the ability to automate the calculation of multiple free-energy differences from a single simulation.
• Validated the preservation of local energy minima in the reference-state Hamiltonian.

Conclusions:

• The novel EDS approach facilitates automated calculation of multiple free-energy differences.
• The method is robust, user-friendly, and grounded in statistical mechanics principles.
• Accelerated EDS offers a reliable tool for computing free-energy differences.