- Graph of van der Waals potential with and without switching
- Graph of electrostatic potential with and without shifting function
- Graph of electrostatic split between short and long range forces
- Graph showing a slice of a ramp potential, showing the effect of
`mgridforcevoff` - Graphical representation of a Colvars configuration.
- Dual topology description for an alchemical simulation. Case example of the mutation of alanine into serine. The lighter color denotes the non-interacting, alternate state.
- Convergence of an FEP calculation. If the ensembles representative
of states
and
are too disparate, equation (67) will
not converge
. If, in sharp contrast, the configurations of state form a subset of the ensemble of configurations characteristic of state , the simulation is expected to converge*(a)*. The difficulties reflected in case*(b)*may be alleviated by the introduction of mutually overlapping intermediate states that connect to*(a)*. It should be mentioned that in practice, the kinetic contribution, , is assumed to be identical for state and state .*(c)* - Relationship of user-defined
to coupling of electrostatic or vdW interactions to a simulation, given specific values of
`alchElecLambdaStart`or`alchVdwLambdaEnd`. - Sample TI data ( against ). The blue shaded area shows the integral with fine sampling close to the end point. The red area shows the difference when values are more sparse. In this example, insufficient sampling before 0.1 can result in a large overestimation of the integral. Beyond 0.2, sparser sampling is justified as dE/d is not changing quickly.
- Schematics of the aMD method. When the original potential (thick line) falls below a threshold energy (dashed line), a boost potential is added. The modified energy profiles (thin lines) have smaller barriers separating adjacent energy basins.
- Schematic illustration of GaMD. When the threshold energy is set to the maximum potential ( mode), the system's potential energy surface is smoothened by adding a harmonic boost potential that follows a Gaussian distribution. The coefficient , which falls in the range of , determines the magnitude of the applied boost potential.
- The core difference between conventional and constant-pH MD can be
illustrated by a simple enzyme
with four protonation states
describing the occupancy of two titratable residues,
and
.
A conventional MD simulation handles the states
*separately*(left panel). The relative importance of the states must be known beforehand or computed by other means. Conversely, a constant-pH MD simulation handles the states*collectively*and actively simulates interconversion (right panel). Determining the relative importance of the states is a direct result of the simulation. - The basic constant-pH MD scheme in NAMD is to alternate equilibrium sampling in a fixed protonation state followed by a nonequilibrium MD Monte Carlo move to sample other protonation states. The latter move can be accepted or rejected. If accepted, the simulation continues in the new protonation state. If the move is rejected, sampling continues as if the move were never attempted at all.
- Hybrid QM/MM NAMD
- Diagram of classical point charge options.
- Treatment of QM/MM bonds
- Charge Groups and QM/MM Bonds
- Diagram of Multiple Grid Regions
- Example of cutoff and pairlist distance uses

http://www.ks.uiuc.edu/Research/namd/