(189v) Biasing High-Dimensional Free-Energy Landscapes for the Detection of Stable Clusters in Self-Assembling Systems
- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Computational Molecular Science and Engineering Forum
- Time: Monday, October 29, 2018 - 3:30pm-5:00pm
In this poster, I will highlight how recently introduced enhanced sampling techniques, which allow users to bias multiple degrees of freedom (or collective variables), make simulations of self-assembling systems tractable.1,2 This method shows rapid convergence of free energies for 3-13 particle Lennard-Jones systems. The simulation trajectories are post-processed to detect stable structural minima and find the relative stability of structures. We apply the same strategy to detect the structure of stable water clusters and amyloid-beta peptide aggregates. In the course of studying these systems, we also comment on the best choice of collective variables for such high-dimensional, self-assembling systems.
(1) Pfaendtner, J.; Bonomi, M. Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics. J. Chem. Theory Comput. 2015, 11 (11), 5062â5067.
(2) Prakash, A.; Fu, C. D.; Bonomi, M.; Pfaendtner, J. Biasing Smarter, Not Harder, By Partitioning Collective Variables Into Families. (Under Review). 2018.