How a Neural Network Speeded Up Molecular Collision Calculations express
Atoms in a gas behave like billiard balls: they fly, collide, and scatter in different directions. But instead of solid balls, they have a soft "coat" of attractive and repulsive forces. At close range, atoms repel sharply; at longer range, they attract slightly. Scientists need to calculate these collisions precisely to model gas flows around airplanes or inside microchips.
Previously, each collision required solving a complex equation, and the computer spent a lot of time on it. Now, researchers trained a neural network on millions of precomputed collisions. The network learned to instantly predict the angle at which atoms will scatter — with an error of less than one-tenth of a degree. Thanks to this, the entire gas calculation sped up by a third.
This approach is especially important for modeling cold and rarefied gases, where attractive forces between atoms noticeably alter the flow pattern. For example, in new calculations, the wake behind a cylinder in a supersonic flow turned out to be wider than in older simplified models. This means that previous simulations may have underestimated the effects of rarefaction and low temperatures.
Now engineers can more quickly test how a gas will behave under unusual conditions — from high-altitude flights to cooled microchannels. And the neural network, trained on collision physics, continues to get faster with each new processor.
The neural network predicts the atomic deflection angle with an error of just 0.0016 radians — about one-tenth of a degree, as if a billiard ball deviated by the thickness of a hair at arm's length.