The GNN architecture is shown in Figure . It is based on our previous work in Ref. but extends the approach for the prediction of energies and forces in multiple solvents. The model represents an invariant GNN with three passes. The additional solvent embedding was implemented using a lookup table that carries a vector of length 64 with learnable weights for each solvent. These weights were optimized during training together with all other trainable parameters. A detailed description of the architecture is given in the SI Section "Graph Neural Network". For the training process, 95% of the full dataset was used while the remaining 5% was reserved for validation. The training was carried out over 50 epochs with a batch size of 256. An exponentially decaying learning rate was applied, ranging from 5 × 10 -4 to 5 × 10 -6 . The mean squared error was selected as the loss function, and the Adam optimizer was employed for weight optimization, with gradients being clipped to a norm of 1. Additionally, a dropout value of 0.1 was employed during training.