Timothée Devergne

Slow dynamical modes from static averages In recent times, efforts have been made to describe the evolution of a complex system not through long trajectories but via the study of probability distributio

#compchem Good read: Slow dynamical modes from static averages #compchemsky doi.org/10.1063/5.02...

Finally, using this spectral decomposition, we can also compute the time evolution of observables, such as the occupation numbers of metastable states. This is shown here for the alanine tetrapeptide molecule, for an initial distribution centered in the least stable state of the molecule.

Each eigenpair gives information about the transition mechanism, shown here for the alanine dipeptide molecule. We even show that good information can be obtained from a simulation biased with a poor collective variable (here the psi angle)

To do so, we only need to compute averages with respect to the Boltzmann distribution, which means that biased simulations can be used by simply reweighting the averages.

In this paper, we model the relaxation from an initial probabiluty distribution towards the Boltzmann one with a Langevin equation. Using this assumption, we learn the associated infinitesimal generator and its spectral decomposition.

Slow dynamical modes from static averages In recent times, efforts have been made to describe the evolution of a complex system not through long trajectories but via the study of probability distributio

I am excited to share with you our new work with Vladimir Kostic, @pontilgroup.bsky.social and Michele Parrinello
doi.org/10.1063/5.02...

1/ 🚀 Over the past two years, our team, CSML, at IIT, has made significant strides in the data-driven modeling of dynamical systems. Curious about how we use advanced operator-based techniques to tackle real-world challenges? Let’s dive in! 🧵👇