Characterizing Metastable States with the Help of Machine Learning

Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature are becoming increasingly challenging. In this paper, we first use the variational approach to conformation dynamics to discover the slowest dynamical modes of the simulations. This allows the different metastable states of the system to be located and organized hierarchically. The physical descriptors that characterize metastable states are discovered by means of a machine learning method. We show in the cases of two proteins, chignolin and bovine pancreatic trypsin inhibitor, how such analysis can be effortlessly performed in a matter of seconds. Another strength of our approach is that it can be applied to the analysis of both unbiased and biased simulations.

Automated summary

In this paper, a variational approach is used to identify the slowest dynamical modes in atomistic simulations, allowing the discovery and hierarchical organization of metastable states. Machine learning is then used to determine the physical descriptors characterizing these states. The efficiency and applicability of this approach is demonstrated through analysis of two proteins. It can be used for both unbiased and biased simulations.