Mean Force Based Temperature Accelerated Sliced Sampling: Efficient Reconstruction of High Dimensional Free Energy Landscapes
Asit Pal, Subhendu Pal, Shivani Verma, Motoyuki Shiga, Nisanth N. Nair

TL;DR
This paper introduces TASS-MF, a mean-force based reweighting scheme that enhances temperature accelerated sliced sampling, allowing accurate high-dimensional free energy landscape reconstruction with fewer windows and no need for WHAM post-processing.
Contribution
The novel TASS-MF method improves efficiency by reducing the number of required windows and eliminating the need for WHAM, enabling accurate high-dimensional free energy calculations.
Findings
Accurately computed free energy landscapes within kcal/mol.
Demonstrated effectiveness on alanine di- and tripeptides.
Outperformed conventional umbrella sampling and metadynamics in high dimensions.
Abstract
Temperature Accelerated Sliced Sampling (TASS) is an efficient method to compute high dimensional free energy landscapes. The original TASS method employs the Weighted Histogram Analysis Method (WHAM) which is an iterative post-processing to reweight and stitch high dimensional probability distributions in sliced windows that are obtained in the presence of restraining biases. The WHAM necessitates that TASS windows lie close to each other for proper overlap of distributions and span the collective variable space of interest. On the other hand, increase in number of TASS windows implies more number of simulations, and thus it affects the efficiency of the method. To overcome this problem, we propose herein a new mean-force (MF) based reweighting scheme called TASS-MF, which enables accurate computation with a fewer number of windows devoid of the WHAM post-processing. Application of the…
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