Simulation memory size

If we assume a maximum available memory size of 4 GB, then the smallest value of a that we could possibly use with our current simulation would be 4 X 10 , though the runtime would be prohibitive. 

Due to the development of advanced numerical methods in the last decades, quantum approaches are now able to accurately describe the chemical bonds formed between two reactants. Nevertheless, when a surface is involved, the actual systems met in practice, for example a dense polymeric layer adsorbed on a rough surface, cannot yet be simulated, because this would require too large a memory size or too long a computation time. Quantum calculations, thus, cannot compete with empirical models in the prediction of adhesion strengths. However, they may allow one to check their validity in model cases, for example small molecules adsorbed on a substrate, or large molecules adsorbed on a cluster of a few atoms which simulates the substrate. This has been done in a number of cases but, to the author s knowledge, mostly for adsorption processes on metallic surfaces. Numerical results for the adsorption of molecules on oxide surfaces may be found in the literature (Henrich and Cox, 1994), but there exists no systematic discussion in the framework of acid-base interactions. 

Despite all the shortcomings listed above, full particle classical MD can be considered mature [84]. Even when all shortcomings will be overcome, we can now clearly delineate the limits for application. These are mainly in the size of the system and the length of the possible simulation. With the rapidly growing cheap computer memory shear size by itself is hardly a limitation several tens of thousands of particles can be handled routinely (for example, we report a simulation of a porin trimer protein embedded in a phospholipid membrane in aqueous environment with almost 70,000 particles [85] see also the contribution of K. Schulten in this symposium) and a million particles could be handled should that be desired. 

The key feature of the systems to be considered in this book is that they have short memories that is, the effects of perturbations diminish with the passage of time. In the example of this chapter, the carbon dioxide pressure returns to a value of 1 within a century or two of the perturbation, regardless of the size of the initial perturbation. In this kind of system, computational errors do not grow as the calculation proceeds instead, the system forgets old errors. That is why the reverse Euler method is useful despite its simplicity and limited accuracy. The many properties of the environment that are reasonably stable and predictable can, in principle, be described by equations with just this kind of stability, and these are the properties that can be simulated using the computational methods described in this book. 

It Is to be remarked that the process described by the Infinite set of kinetic (coagulation) equations can be simulated by Monte-Carlo methods ( ). The Information on the number of molecules of the respective size Is stored In the computer memory and weighting for selection of molecules Is applied given by the number and reactivity of groups In the respective molecule. 

Since this simulation will run for a long time and we have specified a small Maximum Step Size, a lot of data will be collected. PSpice normally collects voltage data at every node and current data through every circuit component This results in a large Probe data file that can take a long time to load and may cause memory problems. Since we are interested only in the input and output voltages, we will tell PSpice to collect data only at the input and output nodes, which will be marked with markers. 

Until the late 80 s, the software development was very much curtailed by the limitations of hardware, where the size of the memory was the most critical factor. Today, about a decade later, the most critical aspect for large scale parallel MD simulation is not the hardware, but the software. Coping with increased problem and algorithmic complexity, as well as, varying hardware platforms is a daunting task. Adding the requirement of optimal use of hardware resources makes the development or modification of an efficient and portable parallel MD simulation software a formidable challenge. 

Difficulties arise even in forward modeling because of the huge size of the numerical problem to be solved for adequate representation of the complex 3-D distribution of EM parameters in the media. As a re.sult, computer simulation time and memory requirements could be excessive even for practically realistic models. Additional difficulties are related to EM imaging which is based on EM inverse problems. These problems are nonlinear and ill-posed, because, in general cases, the solutions can be unstable and/or nonunique. In order to overcome these difficulties one should... 

In contrast with the AB system described above, RDX and most other energetic materials have long reaction times—fractions of a microsecond—and extended reaction zone lengths, on the order of a millimeter. Due to the size of the reaction zone and the complexity of the interatomic potentials necessary to describe real nitramines, steady-state NEMD simulations of detonation are beyond current and near-fixture capabilities, both in computation time and computer memory requirements. Keeping these limitations in mind, we use NEMD to study the initial chemical events in RDX under shock loading. In Section 5 we will describe equilibrium MD simulations to study phenomena at longer time-scales. 

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