Another major challenge in ensuring reliability of MD simulation data is the adequacy of the sampling. In an ideal case, each MD simulation trajectory would be ergodic, that is, the time averages of the properties of interest equal the ensemble averages. Achieving such a condition requires essentially infinite simulation time, which is obviously impossible. Therefore, MD simulation practitioners must achieve sufficient sampling of the energy surface of interest. A single simulation trajectory has a tendency to get "stuck" in a local energy minimum, given that larger conformational changes are unlikely on the typical ns -µs time scale within the limits of thermal fluctuations. One approach to overcoming this challenge is to perform replicate simulations. Just as one would conduct an experiment several times to prove its robustness, so too should MD simulations be repeated. These replicates can be initiated from different random velocities at the outset of the protocol or from different starting configurations of the system. By doing so, each simulation begins at a slightly different point in phase space and therefore may sample somewhat different features of the energy landscape and thus states within the statistical mechanical ensemble, thereby better approximating ergodicity. Other possible solutions involve using biased simulation methods, such that slow degrees of freedom are sampled more quickly and more states are visited. A variety of such approaches exist, including metadynamics, Gaussian-accelerated MD, and weighted ensemble methods. A full description of these techniques is beyond the scope of this Perspective.