MINT32: A Minimum-Image INT32 Coordinate Representation for Fast and Accurate Molecular Dynamics on GPUs.

Molecular dynamics (MD) simulations on GPUs have historically required a trade-off between performance and numerical precision. Mixed-precision approaches, such as AMBER SPFP and OpenMM, accumulate forces with high precision while representing coordinates with single precision (FP32). This design introduces a fundamental "noise floor": quantization error in FP32 coordinates injects artificial heat into the system, degrading the long-term stability and distorting the kinetic properties. We introduce MINT32, a coordinate representation that maps the simulation box onto a 32-bit integer grid. MINT32 achieves a uniform spatial resolution of approximately 0.01 fm, roughly 2 orders of magnitude finer than the worst-case FP32 spacing. This precision comes without the memory bandwidth penalties associated with 64-bit integer arithmetic. In addition, MINT32 offers algorithmic elegance through self-wrapping, whereby periodic boundary conditions are enforced via exact integer overflow, eliminating branch divergence entirely. Benchmark results on three systems spanning 12K to 91K atoms demonstrate that MINT32 reduces energy drift in microcanonical (NVE) simulations by 5-10×, delivering double-precision-level stability for challenging PME water systems and large protein benchmarks alike. When paired with single-precision force evaluations, MINT32 outperforms conventional mixed-precision models, an observation reproduced across two independent protein systems, indicating that coordinate precision is the dominant factor governing simulation stability. The fixed-point grid further supports tightened SHAKE tolerances of 10