“Rebooting” chemical simulations can lead to faster results

A new study from Tel Aviv University (TAU) has found that “pausing and restarting” sampling in chemical simulations can facilitate faster results, enhancing a common practice in information technology.

PhD student Ophir Bloomer led the research in the collaboration. Prof. Shlomi Rivini And Dr. Barak Hirschberg By TAU Sackler School of Chemistry. The study was published in the journal Nature Communications.

Researchers explain this. Molecular Dynamics Simulations are like a virtual microscope. They track the motion of all atoms in chemical, physical and biological systems such as proteins, liquids and crystals. They provide insight into various processes and have various technological applications, including drug design. But these simulations are limited to processes slower than a millionth of a second and cannot describe slow processes such as protein folding and crystal nucleation. This limitation, known as the time scale problem, is a major challenge in the field.

“In our new study we show that the time scale problem can be overcome by stochastic resetting of the simulations,” Bloomer says. “This seems counterintuitive at first glance – how can the simulations end so quickly when they start again? But it turns out that the reaction times vary considerably between simulations. In some simulations, the reaction occurs quickly, but other simulations lose long periods of time in intermediate states. Reordering prevents simulations from getting stuck in such intermediate states and reduces simulation average time.

The researchers also combined stochastic resetting with metadynamics, a popular method for speeding up simulations of slow chemical processes. The combination allows for greater acceleration of both methods when used separately. Furthermore, metadynamics relies on prior knowledge: the reaction coordinates must be known to speed up the simulation. The combination of metadynamics with resetting significantly reduces the dependence on prior information, saving time for practitioners of the method.

Finally, the researchers showed that this combination provides more accurate predictions of slow process rates. The combined approach was used to successfully extend simulations of protein folding in water and is expected to be applied to more systems in the future.