# PET-MAD

Machine-learning interatomic potentials (MLIPs) have largely kept inference in FP32 because molecular dynamics is sensitive to small force errors. For PET, an MLIP from EPFL's Lab COSMO, BF16x9 on Blackwell GPUs and TF32x3 on Ampere GPUs decompose each FP32 matrix-multiply operand into lower-precision components evaluated on Tensor Cores, with no retraining or architecture change. On a 17,280-atom water box, BF16x9 sped PET's large GEMMs by 2.2–2.5x and the full MD step by up to 1.7x; TF32x3 sped major GEMMs on A100 by about 1.7–1.9x and the MD step by up to 1.5x, while keeping energy conservation and structural relaxation close to FP32.

Machine-learning interatomic potentials (MLIPs) have largely kept inference in FP32 because molecular dynamics is sensitive to small force errors. For PET, an MLIP from EPFL's Lab COSMO, BF16x9 on Blackwell GPUs and TF32x3 on Ampere GPUs decompose each FP32 matrix-multiply operand into lower-precision components evaluated on Tensor Cores, with no retraining or architecture change. On a 17,280-atom water box, BF16x9 sped PET's large GEMMs by 2.2–2.5x and the full MD step by up to 1.7x; TF32x3 sped major GEMMs on A100 by about 1.7–1.9x and the MD step by up to 1.5x, while keeping energy conservation and structural relaxation close to FP32.

Chaining machine-learning potentials, accelerated sampling and mass thermodynamic integration into one workflow brings water's autoionization constant to 14.31 against the experimental 13.93 once nuclear quantum effects are included explicitly, and the same workflow computes all six dissociation constants of phosphoric acid without changing the order-parameter definitions, cutting the mean absolute error from 2.19 to 0.45.

Chaining machine-learning potentials, accelerated sampling and mass thermodynamic integration into one workflow brings water's autoionization constant to 14.31 against the experimental 13.93 once nuclear quantum effects are included explicitly, and the same workflow computes all six dissociation constants of phosphoric acid without changing the order-parameter definitions, cutting the mean absolute error from 2.19 to 0.45.