H. J. C. Berendsen and W. F. van Gunsteren. Practical algorithms for dynamic simulations. In G. C. Ciccotti and W. G. Hoover, editors, Proceedings of the International School of Physics, "Enrico Fermi", volume on course 97, pages 43--65, Amsterdam, 1986. North-Holland.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a slower time scale, however, transitions between the two states occur, rendering the conditional distribution bimodal. parameters were chosen as in the aforementioned studies, namely m = 1; ff = 1; kT = 1: Equation (4. 24) was integrated numerically with the leapfrog algorithm (see, for instance, [5]) that is, let x : and F (t) Gamma dV (t) dx Gamma ff x(t Gamma ffi=2) R(t) then x( x(0) F (0) x(t ) x(t Gamma ) F (t)ffi x(t ffi) x(t) x(t )ffi (4.26) The discrete stepsize ffi needs to be selected sufficiently small so as to prevent ....

Berendsen H.J.C., van Gunsteren W.F. (1986): Practical Algorithms for Dynamic Simulations. In: Ciccotti G., Hoover W.G. (eds.), Molecular-Dynamics Simulation of Statistical-Mechanical Systems, North-Holland, Amsterdam.

....states occur, rendering the conditional distribution bimodal. parameters were chosen as in the aforementioned studies, namely m 1, ct 1, kT 1. Equation (4. 24) was integrated numerically with the leapfrog algorithm (see, for dx and F(t) 1 dr(t) ct(t 5 2) R(t) then instance, [5]) that is, let : x ( 0) F(O) k(t ) k(t ) F(t)5 x(t = x(t) 4.26) The discrete stepsize 5 needs to be selected sufficiently small so as to prevent instabilities (the results presented here were obtained with 5 = 0.1) Figure 4.4 depicts a typical segment of the resulting time ....

Berendsen H.J.C., van Gunsteren W.F. (1986): Practical Algorithms for Dynamic Simulations. In: Ciccotti G., Hoover W.G. (eds.), Molecular-Dynamics Simulation of Statistical-Mechanical Systems, North-Holland, Amsterdam.

....on Fig. 3.3 is quite universal for all the regimes. This gives the time exponent of the increase of the energy drift with increasing the time step of the global MD step approximately equal to 3:6. The corresponding time exponent for the conventional Gear5 method, as estimated by Berendsen et al. [2], is approximately 4:8. This shows that both types of the Taylor approximation of the force are considerably less sensitive to the increasing of the time interval at which an actual force evaluation is performed, compared to the integration method they are based on. 3.4. Speedup gained by the MTS ....

H. J. C. Berendsen and W. F. van Gunsteren, Practical algorithms for dynamic simulations, in Molecular Dynamics Simulation of Statistical-Mechanical Systems, G. Ciccotti and W. G. Hoover, eds., North-Holland, New York, 1986, pp. 43-65.

....between the system and a heat bath of temperature T, where k is the Boltzmann constant and a friction coefficient. The parameters were chosen as in the aforementioned studies, namely m = 1; 1; kT = 1: Equation (27) was integrated numerically with the leapfrog algorithm (see, for instance, [1]) Figure 3 depicts a typical segment of the resulting time series, showing fluctuations around two semi stable states on a fast time scale and random transitions between the two states on a much slower one. The problem posed to the network was to predict the probability distribution for the ....

Berendsen H.J.C., van Gunsteren W.F. (1986): Practical Algorithms for Dynamic Simulations. In: Ciccotti G., Hoover W.G. (eds.), Molecular-Dynamics Simulation of Statistical-Mechanical Systems, North-Holland, Amsterdam.

....networks: A summary of the complete scheme The complete sampling scheme can be summarised as follows. Given the values for the momenta and the hyperparameters, the Hamiltonian equations of motion (14) are numerically integrated over L discrete time steps using, for instance, the Leapfrog algorithm (Berendsen and van Gunsteren, 1986). The final configuration is accepted according to equation (18) on the basis of the difference between the final and the initial energy, H(w 2 ) Gamma H(w 1 ) Given the new set of network weights, the hyperparameters are sampled from the posterior distribution (35) and the momenta p i from the ....

Berendsen, H. J. C. and van Gunsteren, W. F. (1986). Practical algorithms for dynamic simulations. In Ciccotti, G. and Hoover, W., editors, Molecular-Dynamics Simulation of Statistical-Mechanical Systems.

....between the system and a heat bath of temperature T, where k is the Boltzmann constant and a friction coefficient. The parameters were chosen as in the aforementioned studies, namely m = 1; 1; kT = 1: Equation (26) was integrated numerically with the leapfrog algorithm (see, for instance, [1]) Figure 3 depicts a typical segment of the resulting time series, showing fluctuations around two semi stable states on a fast time scale and random transitions between the two states on a much slower one. The problem posed to the network was to predict the probability distribution for the ....

Berendsen H.J.C., van Gunsteren W.F. (1986): Practical Algorithms for Dynamic Simulations. In: Ciccotti G., Hoover W.G. (eds.), Molecular-Dynamics Simulation of Statistical-Mechanical Systems, North-Holland, Amsterdam.

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H. J. C. Berendsen and W. F. van Gunsteren. Practical algorithms for dynamic simulations. In G. C. Ciccotti and W. G. Hoover, editors, Proceedings of the International School of Physics, "Enrico Fermi", volume on course 97, pages 43--65, Amsterdam, 1986. North-Holland.

No context found.

H. J. C. Berendsen and W. F. van Gunsteren. Practical algorithms for dynamic simulations. In G. C. Ciccotti and W. G. Hoover, editors, Proceedings of the International School of Physics, "Enrico Fermi", volume on course 97, pages 43--65, Amsterdam, 1986. North-Holland.

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