Interpreting Fermi surface data requires specialized expertise and is vulnerable to noise, creating a bottleneck in materials research. Researchers now demonstrate a machine learning approach that can analyze simulated Fermi surface images of the alloy Co2MnGaxGe1-x and automatically identify compositions where the Fermi surface morphology changes, even in blurred or noisy data. The method reduces the need for manual screening and could accelerate the discovery of advanced electronic materials.