Fig. 2.

Schematic illustration of the OrbNet-Equi method. The input atomic orbital features $\mathbf{T}[{\mathrm{\Psi }}_0]$ are obtained from a low-fidelity QM simulation. A neural network termed UNiTE first initializes atom-wise representations through the diagonal reduction module and then, updates the representations through stacks of block convolution, message-passing, and point-wise interaction modules. A programmed pooling layer reads out high-fidelity property predictions $\widehat{\mathbf{y}}$ based on the final representations. Neural network architecture details are provided in Materials and Methods.