English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This report presents the application of polynomial regression for estimatingfree energy differences using thermodynamic integration. We employ linearregression to construct a polynomial that optimally fits the thermodynamicintegration data, and thus reduces the bias and uncertainty of the resultingfree energy estimate. Two test systems with analytical solutions were used toverify the accuracy and precision of the approach. Our results suggest thatregression with a high degree of polynomials give the most accurate free energydifference estimates, but often with a slightly larger variance, compared tocommonly used quadrature techniques. High degrees of polynomials possess theflexibility to closely fit the thermodynamic integration data but are oftensensitive to small changes in data points. To further improve overall accuracyand reduce uncertainty, we also examine the use of Chebyshev nodes to guide theselection of non-equidistant lambda values for the thermodynamic integrationscheme. We conclude that polynomial regression with non-equidistant lambdavalues delivers the most accurate and precise free energy estimates forthermodynamic integration data. Software and documentation is available athttp://www.phys.uidaho.edu/ytreberg/software

Reducing the Bias and Uncertainty of Free Energy Estimates by Using Regression to Fit Thermodynamic Integration Data