When Machine Learning Loses Its Way: Symmetry’s Role in Predicting Material States
![The model describes a system where electrons traverse a lattice-defined by intra-cell hopping amplitudes [latex]\{t_{1,1}, t_{1,2}, t_{1,3}, ...\}[/latex] and inter-cell amplitudes [latex]\{t_{2,1}, t_{2,2}, t_{2,3}, ...\}[/latex]-supplemented by a long-range hopping term [latex]t_3[/latex] and modulated by on-site potentials [latex]\{V_1, V_2, V_3, ...\}[/latex], enabling electron transport across the structure.](https://arxiv.org/html/2603.11688v1/x1.png)
A new study reveals that convolutional neural networks struggle to accurately classify topological materials when key symmetries are disrupted, hindering their ability to generalize beyond familiar configurations.



