Researchers from MIT and the University of Basel have developed a machine-learning framework that can automatically map out phase diagrams for novel physical systems.

This physics-informed approach is more efficient than manual techniques and does not require large, labeled training datasets.

The framework could help scientists investigate thermodynamic properties of novel materials or detect entanglement in quantum systems.

The goal is to make it possible for scientists to discover unknown phases of matter autonomously.

The Julia Programming Language, a popular scientific computing language, is used to build discriminative classifiers.