Performance-driven design of machine-learned interatomic potentials

Machine-learned interatomic potentials deliver close to density-functional accuracy at a small fraction of the cost, but they are still orders of magnitude more computationally expensive than classical force fields, which keeps most biomolecular simulation out of reach. Much of that cost is not intrinsic to the physics but is due to operations that were designed for model expressiveness and accuracy. We present the SPLine Interatomic Cluster Expansion (SPLICE), an equivariant message-passing architecture designed for inference speed. Its radial basis is a per-channel spline carrying two learned parameters, which has a closed form and is evaluated inside the message-passing kernel. We also decouple the width of the per-edge tensor product from that of the node features, so that the model capacity can grow with the least impact on inference performance, leading to a family of models along a speed and accuracy front. Trained on biomolecular data, the model family spans 0.2 to 4 μs per atom per step on a single NVIDIA H100 80GB GPU, with the fastest models handling more than a million atoms. We evaluate it on conformer energies, on physically motivated benchmarks, and on condensed-phase dynamics of a solvated protein and show on par accuracy with the state-of-the-art models.