Fourier-KAGAT: resolving activity cliffs in organic photocatalysts via Fourier-based learnable activations (opens in new tab)

The discovery of organic photocatalysts is fundamentally limited by the vastness of chemical space and the scarcity of standardized experimental data. Conventional graph neural networks often fail to navigate this landscape, particularly at “activity cliffs” where structural isomers with identical 2D topologies but distinct 3D electronic environments exhibit drastically different catalytic performance. Here, we introduce a Fourier-based Kolmogorov-Arnold Graph Attention Network (Fourier-KAGAT...