Computational experiment

Verlet L 1967 Computer experiments on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules Phys. Rev. f59 98-103... 

Evans D J 1983 Computer experiment for nonlinear thermodynamics of Couette flow J. Chem. Phys. 78 3297-302... 

Hoover W G and Ree F H 1967 Use of computer experiments to locate the melting transition and calculate the entropy in the solid phase J. Chem. Phys. 47 4873-8... 

Murtagh B A and Sargent R W 1970 Computational experience with quadratically convergent minimisation methods Comput. J. 13 185... 

In recent years, computational testimonies for the existence of conical intersections in many polyatomic systems became abundant and compelling [6-11]. The current consensus concerning the ubiquitous presence of conical intersections in polyatomic molecules is due in large part to computational experiments. ... 

Verlet, L. Computer experiments on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules. Phys. Rev. 165 (1967) 98-103. Ryckaert, J.-P., Ciccotti,G., Berendsen, H.J.C. Numerical integration of the cartesian equations of motion of a system with constraints Molecular dynamics of n-alkanes. Comput. Phys. 23 (1977) 327-341. 

Verlet, L. Computer Experiments" on Classical Fluids. I. Thermodynamical Properties of Lennard-Jones Molecules. Physical Review 159 (1967) 98-103 Janezic, D., Merzel, F. Split Integration Symplectic Method for Molecular Dynamics Integration. J. Chem. Inf. Comput. Sci. 37 (1997) 1048-1054 McLachlan, R. I. On the Numerical Integration of Ordinary Differential Equations by Symplectic Composition Methods. SIAM J. Sci. Comput. 16 (1995) 151-168... 

L 1967. Computer Experiments on Classical Fluids. II. Equilibrium Correlation Functions. tysical Review 165 201-204. 

Boero M, M Parrinello and K Terakura 1999. Ziegler-Natta Heterogeneous Catalysis by First Piincip Computer Experiments. Surface Science 438 1-8. 

In analytical chemistry, a number of identical measurements are taken and then an error is estimated by computing the standard deviation. With computational experiments, repeating the same step should always give exactly the same result, with the exception of Monte Carlo techniques. An error is estimated by comparing a number of similar computations to the experimental answers or much more rigorous computations. 

The functionality available in MedChem Explorer is broken down into a list of available computational experiments, including activity prediction, align/ pharmacophore, overlay molecules, conformer generation, property calculation, and database access. Within each experiment, the Web system walks the user through a series of questions that must be answered sequentially. The task is then submitted to a remote server, where it is performed. The user can view the progress of the work in their Web browser at any time. Once complete, the results of the calculation are stored on the server. The user can then run subsequent experiments starting with those results. The Web interface includes links to help pages at every step of the process. 

In his early survey of computer experiments in materials science , Beeler (1970), in the book chapter already cited, divides such experiments into four categories. One is the Monte Carlo approach. The second is the dynamic approach (today usually named molecular dynamics), in which a finite system of N particles (usually atoms) is treated by setting up 3A equations of motion which are coupled through an assumed two-body potential, and the set of 3A differential equations is then solved numerically on a computer to give the space trajectories and velocities of all particles as function of successive time steps. The third is what Beeler called the variational approach, used to establish equilibrium configurations of atoms in (for instance) a crystal dislocation and also to establish what happens to the atoms when the defect moves each atom is moved in turn, one at a time, in a self-consistent iterative process, until the total energy of the system is minimised. The fourth category of computer experiment is what Beeler called a pattern development... 

Beeler defined the broad scope of computer experiments as follows Any conceptual model whose definition can be represented as a unique branching sequence of arithmetical and logical decision steps can be analysed in a computer experiment... The utility of the computer... springs mainly from its computational speed. But that utility goes further as Beeler says, conventional analytical treatments of many-body aspects of materials problems run into awkward mathematical problems computer experiments bypass these problems. 

One other remark of Vineyard s in 1972, made with evident feeling, is worth repeating here Worthwhile computer experiments require time and care. The easy understandability of the results tends to conceal the painstaking hours that went into conceiving and formulating the problem, selecting the parameters of a model. 

It should be observed that every element except the powder system in the recovery system is chosen for favorable shock properties which can be confidently simulated numerically. The precise sample assembly procedures assure that the conditions calculated in the numerical simulations are actually achieved in the experiments. The influence of various powder compacts in influencing the shock pressure and mean-bulk temperature must be determined in computer experiments in which various material descriptions are used. Fortunately, the large porosity (densities from 35% to 75% of solid density) leads to a great simplification in that the various porous samples respond in the same manner due to the radial loading introduced from the porous inclusion in the copper capsule. 

Thus, in order to reproduce the effect of an experimentally existing activation barrier for the scission/recombination process, one may introduce into the MC simulation the notion of frequency , lo, with which, every so many MC steps, an attempt for scission and/or recombination is undertaken. Clearly, as uj is reduced to zero, the average lifetime of the chains, which is proportional by detailed balance to Tbreak) will grow to infinity until the limit of conventional dead polymers is reached. In a computer experiment Lo can be easily controlled and various transport properties such as mean-square displacements (MSQ) and diffusion constants, which essentially depend on Tbreak) can be studied. 

The sense of this procedure may be verified in Figure 4.13. An implicit assumption in this procedure is that the speed at which the sources are activated equals the speed at which the activation zone is propagated. This holds only if the flame propagates into a quiescent mixture, which does not really happen. Computational experiments with the proposed model show that this assumption is increasingly justified as a cloud s aspect ratio increases. 

This ranking exercise can be assigned to one or two team members as a subtask. Consider selecting a teammate with experience in facility operations to compile the necessary data and one with process safety and computer experience to run the models. The resulting report can then be shared with the full team and included in the plan you submit to your management. 

Judging by these results the angular momentum relaxation in a dense medium has the form of damped oscillations of frequency jRo = (Rctc/to)i and decay decrement 1/(2tc). This conclusion is quantitatively verified by computer experiments [45, 54, 55]. Most of them were concerned with calculations of the autocorrelation function of the translational velocity v(t). However the relation between v(t) and the force F t) acting during collisions is the same as that between e> = J/I and M. Therefore, the results are qualitatively similar. In Fig. 1.8 we show the correlation functions of the velocity and force for the liquid state density. Oscillations are clearly seen, which point to a regular character of collisions and non-Markovian nature of velocity changes. 

It is noticed that the Green s function A (x) has one singularity when Xy = 0, but it can be eliminated by an integration over the element around the point Xy=0. A computation experiment shows that Eq (30) may result in a significant numerical error when coarser grids are employed. 

We can arrive at our theories in two main ways. In the first, as illustrated earlier, we subject a system to experimental perturbations, tests, and intrusions, thereby leading to patterns of observables from which we may concoct a theory of the system s structure and function. An alternative approach, made possible by the dramatic advances that have occurred in the area of computer hardware in recent times, is to construct a computer model of the system and then to carry out simulations of its behavior under different conditions. The computer experiments can lead to observables that may be interpreted as though they were derived from interactions. 

There are therefore four adjustable parameters per atom in the refinement (xy, yy, Zj, By). In the computer experiments we have carried out to test the assumptions of the nucleic acid refinement model we have generated sets of observed structure factors F (Q), from the Z-DNA molecular dynamics trajectories. The thermal averaging implicit in Equation III.3 is accomplished by averaging the atomic structure factors obtained from coordinate sets sampled along the molecular dynamics trajectories at each temperature ... 

Molecular dynamics (MD) permits the nature of contact formation, indentation, and adhesion to be examined on the nanometer scale. These are computer experiments in which the equations of motion of each constituent particle are considered. The evolution of the system of interacting particles can thus be tracked with high spatial and temporal resolution. As computer speeds increase, so do the number of constituent particles that can be considered within realistic time frames. To enable experimental comparison, many MD simulations take the form of a tip-substrate geometry correspoudiug to scauniug probe methods of iuvestigatiug siugle-asperity coutacts (see Sectiou III.A). 

Wang RX, Wang SM. How does consensus scoring work for virtual library screening An idealized computer experiment. J Chem Inf Comput Sci 2001 41 1422-6. 

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