Prior to arriving at the simple architecture shown in Figure , more complex models inspired by the work of Pathak et al. were tested. Additional separate latent layers were added to solute and solvent which allowed the network to learn a soluteand solvent-specific representation before performing a configurable 'interaction' operation such as concatenation or element-wise multiplication. The same optimization framework mentioned above was used to choose the best network with these options, and it consistently disabled these additional complexities. Simply relying on Universal Approximation theorem is sufficient -additional inductive bias does not improve performance. See B for further details.