7. Evaluating an extension of transfer learning strategies to more than two fidelities. More specifically, we demonstrate that multiple lower-level fidelity inputs can be successfully integrated and that they can work synergistically, improving performance of downstream models when used jointly compared to individually incorporating them. In this suite of experiments, we use SchNet as the underlying architecture, further validating the effectiveness of transfer learning strategies across a different and widely-used family of GNNs (Section 2.6).