Efficient Exploration of High Dimensional Free Energy Landscapes Employing Parallel Bias Temperature Accelerated Sliced Sampling (PBTASS) Approach.

 

Abhinav Gupta, Shivani Verma and Nisanth N. Nair

 

Department of Chemistry, Indian Institute of Technology, Kanpur-208016, India

 

Computing free energy surface (FES) is critical to predict the kinetics and mechanism of a chemical process. Enhanced sampling of collective variables (CVs) is used widely to explore the FES. Conventional enhanced sampling approaches such as metadynamics (MTD) [1], and umbrella sampling [2] are limited to small number of CVs, often one or two. The sampling efficiency of these methods depreciates with increasing number of CVs. Many methods have been designed to overcome these limitations like bias-exchange MTD, driven-adiabatic free energy dynamics (d-AFED)/ temperature-accelerated molecular dynamics (TAMD), parallel bias MTD (PBMTD), well-sliced MTD, temperature accelerated sliced sampling (TASS) [3]. Here, we propose a new method that combines the aforementioned methods to explore the multidimensional FES which we named as Parallel bias temperature accelerated slice sampling (PBTASS). This method uses parallel bias MTD, which simultaneously applies low dimensional bias along many CVs, instead of MTD in the TASS method. This makes PBTASS more useful in exploring multidimensional FES.

 

REFERENCES:

[1] Laio A. and Parrinello M., Proc. Natl. Acad. Sci. 2002, 99, 12562.

[2] Torrie G. M. and Valleau J. P., J. Comp. Phys. 1977, 187.

[3] Awasthi S. and Nair N. N., WIREs Comput Mol Sci. 2018;1398.