Annealing time, molecular dynamics

D. D. Humphreys, R. A. Friesner, and B. J. Berne. Simulated annealing of a protein in a continuum solvent by multiple-time-step molecular dynamics. J. Phys. Chem., 99 10674-10685, 1995. 

Humphreys D D, R A Friesner and B ] Berne 1995. Simulated Annealing of a Protein in a Continuu Solvent by Multiple Time-step Molecular Dynamics. Journal of Physical Chemistry 99 10674-1068... 

Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation.

The particular iterative technique chosen by Car and Parrinello to iteratively solve the electronic structure problem in concert with nuclear motion was simulated annealing [11]. Specifically, variational parameters for the electronic wave function, in addition to nuclear positions, were treated like dynamical variables in a molecular dynamics simulation. When electronic parameters are kept near absolute zero in temperature, they describe the Bom-Oppenheimer electronic wave function. One advantage of the Car-Parrinello procedure is rather subtle. Taking the parameters as dynamical variables leads to robust prediction of values at a new time step from previous values, and cancellation in errors in the value of the nuclear forces. Another advantage is that the procedure, as is generally true of simulated annealing techniques, is equally suited to both linear and non-linear optimization. If desired, both linear coefficients of basis functions and non-linear functional parameters can be optimized, and arbitrary electronic models employed, so long as derivatives with respect to electronic wave function parameters can be calculated. 

Figure 4. Nuclear trajectories generated by the simulated annealing molecular dynamics defined by the Lagrangian in Equation(4) using masses for all parameters equal to either 0.1 or 0.01 times the proton mass. For comparison, the exact nuclear trajectory on the Born-Oppenheimer surface is shown.

Successful first-principles molecular dynamics simulations in the Car-Paxrinello framework requires low temperature for the annealed electronic parameters while maintaining approximate energy conservation of the nuclear motion, all without resorting to unduly small time steps. The most desirable situation is a finite gap between the frequency spectrum of the nuclear coordinates, as measured, say, by the velocity-velocity autocorrelation function. 

Selloni et al. [48] were the first to simulate adiabatic ground state quantum dynamics of a solvated electron. The system consisted of the electron, 32 K+ ions, and 31 Cl ions, with electron-ion interactions given by a pseudopotential. These simulations were unusual in that what has become the standard simulating annealing molecular dynamics scheme, described in the previous section, was not used. Rather, the wave function of the solvated electron was propagated forward in time with the time-dependent Schrodinger equation,... 

Conformational searching Theoretical methods of conformational analysis are applied to explore the conformational energy surface of molecules, e.g., to find the minimal energy conformation or the bioactive conformation. Conformational changes are based on time-dependence (molecular dynamics), probability (Monte Carlo), or systematic searches, followed by minimization steps or simulated annealing. 

Using the molecular dynamics approach for simulated annealing, after the atomic positions have evolved for several hundred time steps, one cools the system a bit by multiplying each velocity component by a scale factor that is slightly less than one. After another several hundred time steps, one again reduces the velocity components. This is repeated until the temperature is very low, typically 50 K. 

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