Heat flows through matter like water through pipes: sometimes fast, sometimes barely. Engineers seek materials that almost don't let heat pass — like clogged pipes. Such insulators are vital for energy saving and devices that turn heat into electricity. The neural network KappaFormer quickly evaluates the "throughput" of an atomic structure.

KappaFormer's secret lies in its physical intuition. Ludwig Boltzmann realized that heat depends on the chaos in particle motion. In a crystal, atoms quiver; synchronized vibrations carry heat, chaotic ones don't. The network distinguishes these contributions by analyzing the atomic pattern. It was trained on millions of data points about the compression and stretching of materials, and then fine-tuned on rare examples of thermal conductivity. Amazingly, this was enough for ultra-precise predictions — like a plumber detects a clog by the sound of the pipe.

After checking thousands of options, KappaFormer found substances with near-zero heat leakage — hundreds of times better than window glass. Their atomic geometry entangles thermal vibrations, and they die out like sound in an untuned piano. Now the network is being retargeted to search for other materials. The physics stitched into the algorithm leads to materials of the future — from space shields to quantum computers.