$\texttt{FlashSchNet}$: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics

Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain slower than classical force fields due to fragmented kernels and memory-bound pipelines that underutilize GPUs. We show that a missing principle is making GNN-MD $\textit{IO-aware}$, carefully accounting for reads and writes between GPU high-bandwidth memory (HBM) and on-chip SRAM. We present $\texttt{FlashSchNet}$, an efficient and accurate IO-aware SchNet-style GNN-MD framework built on four techniques: (1) $\textit{flash radial basis}$, which fuses pairwise distance computation, Gaussian basis expansion, and cosine envelope into a single tiled pass, computing each distance once and reusing it across all basis functions; (2) $\textit{flash message passing}$, which fuses cutoff, neighbor gather, filter multiplication, and reduction to avoid materializing edge tensors in HBM; (3) $\textit{flash aggregation}$, which reformulates scatter-add via CSR segment reduce, reducing atomic writes by a factor of feature dimension and enabling contention-free accumulation in both forward and backward passes; (4) channel-wise 16-bit quantization that exploits the low per-channel dynamic range in SchNet MLP weights to further improve throughput with negligible accuracy loss. On a single NVIDIA RTX PRO 6000, $\texttt{FlashSchNet}$ achieves $\textbf{1000 ns/day}$ aggregate simulation throughput over 64 parallel replicas on coarse-grained (CG) protein containing 269 beads ($\textbf{6.5}$ $\mathbf{\times}$ faster than CGSchNet baseline with $\textbf{80\\% less}$ peak memory), surpassing widely used classical force fields ($\textit{e.g.}$, MARTINI) while retaining SchNet-level accuracy and transferability.

Molecular dynamics simulations help scientists study how proteins and other molecules move, which is important for drug discovery and materials science. New AI-based models can make these simulations more accurate, but they are often too slow for long simulations because modern graphics processors spend too much time moving data around instead of doing useful calculations. We developed FlashSchNet, a faster version of a neural-network model for molecular simulation, by reorganizing the computation so that data are reused inside the GPU rather than repeatedly written to and read from memory. We also replace a slow accumulation step with a more orderly one and use lower-precision arithmetic where it does not hurt accuracy. In our protein simulations, FlashSchNet runs much faster and uses far less memory while preserving the behavior of the original model, making accurate AI-driven molecular simulation more practical for researchers with limited computing resources.