|
The most important part is at the end: According to Agnieszka Grabska-Barwinska, a member of the team, the graph neural network learned to encode a pattern that physicists call correlation length. That is, as DeepMind’s graph neural network restructured itself to reflect the training data, it came to exhibit the following tendency: When predicting propensities at higher temperatures (where molecular movement looks more liquid-like than solid), for each node’s prediction the network depended on information from neighboring nodes two or three connections away in the graph. But at lower temperatures closer to the glass transition, that number — the correlation length — increased to five. “We see that the network extracts, as we lower the temperature, information from larger and larger neighborhoods” of particles, said Thomas Keck, a physicist on the DeepMind team. “At these different temperatures, the glass looks, to the naked eye, just identical. But the network sees something different as we go down.” Increased correlation length is a hallmark of phase transitions, in which particles transition from a disordered to an ordered arrangement or vice versa. It happens, for instance, when atoms in a block of iron collectively align so that the block becomes magnetized. As the block approaches this transition, each atom influences atoms farther and farther away in the block. |