Finite-sample bias in free energy bridge estimators

Area of Science:

• Computational chemistry
• Statistical mechanics

Background:

• Free energy estimation is crucial in molecular simulations.
• Bias in estimators is often neglected in the large-sample limit.
• Finite-sample bias requires careful consideration for accurate results.

Purpose of the Study:

• To develop a metric for quantifying finite-sample bias in free energy bridge estimators.
• To provide a framework for comparing systematic and statistical errors.
• To highlight the significance of bias in modest sample sizes.

Main Methods:

• Developed a novel bias metric applicable to a broad class of free energy bridge estimators.
• Integrated the bias metric with existing variance estimation techniques.
• Applied the framework to illustrative examples.

Main Results:

• Demonstrated that finite-sample bias can be substantial, contrary to common assumptions.
• Provided a quantitative measure to assess systematic error in free energy calculations.
• Showcased the utility of the bias metric in practical scenarios.

Conclusions:

• Finite-sample bias is a critical factor in free energy estimation that should not be ignored.
• The developed bias metric offers a valuable tool for rigorous error analysis in computational studies.
• Researchers should account for bias, especially when working with limited sample sizes.