GPUMD-PySAGES: GPU-Native Enhanced Sampling on Machine-Learning Potentials
The rare-event problem In standard MD, the probability of observing a configuration $\mathbf{R}$ at temperature $T$ follows the Boltzmann distribution: $$P(\mathbf{R}) \propto \exp\left(-\frac{U(\mathbf{R})}{k_B T}\right)$$ where $U(\mathbf{R})$ is the potential energy and $k_B$ is Boltzmann’s constant. Transitions between metastable states separated by free-energy barriers $\Delta G^\dagger \gg k_B T$ are exponentially rare — the mean first-passage time scales as $\tau \sim \tau_0 \exp(\Delta G^\dagger / k_B T)$. For barriers above ~1 eV, these events become effectively inaccessible on MD timescales....