Alongside methods that leverage currently explored states or generative models to seed new starting simulations, many methods rapidly sample energy landscapes by altering the Hamiltonian of the system. By flattening the effective energy landscape of the system, simulations will traverse between states more efficiently and enable exploration within less wall time. Such methods must be analyzed with care, as Chapter the altered Hamiltonian means that raw statistics are not reflective of the true distribution of the underlying energy landscape. Many clever backmapping and correction tools have been developed to correct for the inherent bias in the landscape, but these are mainly used to obtain appropriate thermodynamic populations and parameters. An altered Hamiltonian of the system means that the kinetics of a system are going to be biased and never reflect the true rates or kinetics of the system. As a result, one should also consider pairing these approaches with adaptive seeding like approaches, which can enable rapid state discovery, thermodynamic population estimation, and kinetic rate prediction to a reasonable degree . Some enhanced sampling methods are inherently parallelizable, since altering the Hamiltonian only happens on a per-replica basis. Multiple asynchronous replicas can be then pooled together to generate unified projections of the explored state space. Replica exchange Hamiltonian Replica Exchange (HREX) and Replica Exchange with Solute Tempering (REST) are powerful methods for enhancing sampling by allowing replicas of the system to explore different Hamiltonians or temperatures . In parallel architectures with connected nodes, these replicas can be run independently, with periodic exchanges between them to ensure broader sampling of the energy landscape. Such methods have already demonstrated a great capability to augment drug discovery and study the impact of mutations . However, HREX and REST are often challenging to parallelize efficiently because exchanges require synchronization between replicas, which can limit the full potential of asynchronous parallelization. Expanded ensemble approaches, where a single replica explores a range of Hamiltonians or temperatures without needing exchanges, can offer a more parallel-friendly solution while maintaining the advantages of enhanced sampling . Metadynamics Metadynamics modifies the energy landscape by adding bias potentials over time usually in the form of Gaussians on a collective variable (CV), encouraging the system to explore new conformational states while discouraging known states from being oversampled. This method is especially powerful when paired with adaptive seeding approaches, where different starting conditions are used to explore a wide range of states in parallel . Each simulation can run asynchronously and independently, with the accumulated bias potentials integrated to generate a unified view of the free energy landscape. This flexibility can run both ways, with parallel metadynamics simulations being seeded from multiple states, or with metadynamics simulations being used to generate seeds for equilibrium simulations. The flexibility of parallelization in metadynamics makes it ideal for massively parallel simulations, allowing for both the exploration of new states and refinement of previously visited regions. Ease of analysis is also a highlighting point for metadynamics: All one has to do is track the biases added along the collective variable of interest across all simulations and pool them together to capture the energy minima explored. Accelerated MD Accelerated Molecular Dynamics (aMD) introduces a bias to flatten energy barriers, enabling the system to explore faster than in standard molecular dynamics . In the context of massively parallel simulations, multiple replicas can run independently, each experiencing a different flattening bias. These independent simulations can be pooled into a single ensemble, where the accelerated dynamics can be reweighted to recover an unbiased free energy landscape. The asynchronous nature of aMD allows for highly efficient parallelization, making it a powerful method for exploring large conformational spaces. One can also explore multiple biasing functions that flatten the landscape: a modified version called Gaussian Accelerated MD (GaMD) uses a Gaussian to dampen the energy of the system, smoothing it in a way that ensures the bias is applied more evenly . This approach enhances sampling even further by preventing the system from becoming trapped in local minima, while still allowing for a more accurate reconstruction of the underlying free energy surface. Lastly, pairing replica exchange methods with aMD methods to sample the impact of different biasing functions at once has promising potential to generate highly converged landscape projections . Umbrella sampling Umbrella sampling divides the phase space into overlapping windows, each constrained by a bias potential to sample a specific region. Usually, a harmonic constraint or something similar is used to constrain the system to remain within the window of choice . This approach offers embarrassing parallelism: multiple windows can be run asynchronously and independently with the results from each simulation integrated afterward using techniques like the Weighted Histogram Analysis Method (WHAM) to reconstruct a unified free energy landscape . The final energy landscape is thus built by combining data across multiple windows and can consider multiple constraint strengths as well as a broad range of phase space. Umbrella sampling is particularly useful for exploring energy barriers and reconstructing accurate thermodynamic profiles for capturing rare events .