SSAGES: Software Suite for Advanced General Ensemble Simulations

• Published in: The Journal of chemical physics 2018-01, Vol.148 (4), p.044104-044104
• Main Authors: Sidky, Hythem, Colón, Yamil J., Helfferich, Julian, Sikora, Benjamin J., Bezik, Cody, Chu, Weiwei, Giberti, Federico, Guo, Ashley Z., Jiang, Xikai, Lequieu, Joshua, Li, Jiyuan, Moller, Joshua, Quevillon, Michael J., Rahimi, Mohammad, Ramezani-Dakhel, Hadi, Rathee, Vikramjit S., Reid, Daniel R., Sevgen, Emre, Thapar, Vikram, Webb, Michael A., Whitmer, Jonathan K., de Pablo, Juan J.
• Format: Article
• Language: English
• Subjects: Adaptive sampling; Computer simulation; Free energy; Mathematical models; MATHEMATICS AND COMPUTING; Molecular dynamics; Sampling methods; Simulation; Software; Software packages
• ISSN: 0021-9606; 1089-7690
• DOI: 10.1063/1.5008853

Molecular simulation has emerged as an essential tool for modern-day research, but obtaining proper results and making reliable conclusions from simulations requires adequate sampling of the system under consideration. To this end, a variety of methods exist in the literature that can enhance sampling considerably, and increasingly sophisticated, effective algorithms continue to be developed at a rapid pace. Implementation of these techniques, however, can be challenging for experts and non-experts alike. There is a clear need for software that provides rapid, reliable, and easy access to a wide range of advanced sampling methods and that facilitates implementation of new techniques as they emerge. Here we present SSAGES, a publicly available Software Suite for Advanced General Ensemble Simulations designed to interface with multiple widely used molecular dynamics simulations packages. SSAGES allows facile application of a variety of enhanced sampling techniques—including adaptive biasing force, string methods, and forward flux sampling—that extract meaningful free energy and transition path data from all-atom and coarse-grained simulations. A noteworthy feature of SSAGES is a user-friendly framework that facilitates further development and implementation of new methods and collective variables. In this work, the use of SSAGES is illustrated in the context of simple representative applications involving distinct methods and different collective variables that are available in the current release of the suite. The code may be found at: https://github.com/MICCoM/SSAGES-public.