If your computer gets stuck, users naturally press keys for a restart.
Researchers at Tel Aviv University (TAU) have found that this well-known practice in information technology can also be applied to chemistry – to enhance the sampling in chemical simulations, all one needs to do is stop and restart.
The team members explained that molecular dynamics simulations are like a virtual microscope, explaining the motion of all atoms in chemical, physical, and biological systems such as proteins, liquids, and crystals.
They provide insights into various processes and have different technological applications, including drug design.
However, these simulations are limited to processes slower than one-millionth of a second and thus cannot describe slower processes such as protein folding and crystal nucleation. This limitation, known as the timescale problem, is a great challenge in the field.
Details of the study
The research was led by doctoral student Ofir Blumer in collaboration with Prof. Shlomi Reuveni and Dr. Barak Hirshberg from the School of Chemistry at Tel Aviv University. The study was published in the prestigious journal Nature Communications under the title “Combining stochastic resetting with metadynamics to speed up molecular dynamics simulations.”
“In our new study, we show that the timescale problem can be overcome by stochastic (randomly determined) resetting of the simulations. It seems counterintuitive at first glance – how can the simulations end faster when restarted?” Blumer said.
“Yet, it turns out that reaction times vary considerably between simulations. In some simulations, reactions occur rapidly, but other simulations get lost in intermediate states for long periods. Resetting prevents the simulations from getting stuck in such intermediates and shortens the average simulation time.”
The researchers also combined stochastic resetting with metadynamics, a popular method to speed up the simulations of slow chemical processes.
The combination allows greater acceleration than either method separately. Moreover, metadynamics relies on prior knowledge: the reaction coordinates must be known to expedite the simulation. The combination of metadynamics with resetting reduces the dependency on prior knowledge significantly, saving time for practitioners of the method.
Finally, the researchers showed that the combination provides more accurate predictions of the rate of slow processes. The combined method was used to enhance simulations of a protein folding in water successfully, and it is expected to be applied to more systems in the future.