Computational strategy, steps

To perform simulations of relatively large systems for relatively long times, it is essential to optimize the computational strategy of discrete particle simulations. Obviously, the larger the time step 5t, the more efficient the simulation method. For the soft-sphere model, the maximum value for 5t is dictated by the duration of a contact. Since there are two different spring-dashpot systems in our current model, it is essential to assume that tcontact>n — tcontacUU so that... 

As a further step currently under investigation, the relationship between local polarizability and local softness is studied with the aim to substitute atom-in-molecule polarizabilities by atom-condensed softness values. In this way, conceptual DFT could be exploited in a computational strategy, an ansatz rarely used until now, the best known example being the electronegativity equalization method [101]. 

These results suggest a computational strategy for the study of reactions in condensed phases. One starts from some realistic intermolecular potentials and performs a molecular-dynamics-Kramers-Grote-Hynes scheme that consists of the following steps.First, we fix the proton at the transition state and run a MD simulation. The friction kernel y(t) is calculated and along with Eqs. (7,8) enables the calculation of the Grote-Hynes rate. This scheme has also been used as a means of obtaining input for quantum calculations as well. ... 

A step forward along the route to the correct modelling of the spectroscopy and photochemical reactivity of photoreactive proteins is represented by the implementation of a Quantum Mechanics/Molecular Mechanics (QM/MM) computational strategy based on a suitable QM part coupled with a protein force field such as AMBER [34] (or CHARMM [35]). Very recently a CASPT2//CASSCF/AMBER method for rhodopsin has been implemented in our laboratory [36,37] within the QM/MM hnk-atom scheme [38]. Special care has been taken in the parametrization of the protonated Schiff base linkage region that describes the dehcate border region between the MM (the protein)... 

Computational issues that are pertinent in MD simulations are time complexity of the force calculations and the accuracy of the particle trajectories including other necessary quantitative measures. These two issues overwhelm computational scientists in several ways. MD simulations are done for long time periods and since numerical integration techniques involve discretization errors and stability restrictions which when not put in check, may corrupt the numerical solutions in such a way that they do not have any meaning and therefore, no useful inferences can be drawn from them. Different strategies such as globally stable numerical integrators and multiple time steps implementations have been used in this respect (see [27, 31]). 

We can obtain a crude estimate the time required for a precise quantum mechanical calculation to analyse possible syntheses of bryosta-tin. First, the calculation of the energy of a molecule of this size will take hours. Many such calculations will be required to minimise the energy of a structure. A reasonable estimate may be that a thousand energy calculations would be required. Conformation searching will require many such minimisations, perhaps ten thousand. The reactivity of each intermediate will require a harder calculation, perhaps a hundred times harder. Each step will have many possible combinations of reagents, temperatures, times, and so on. This may introduce another factor of a thousand. The number of possible strategies was estimated before as about a million, million, million. In order to reduce the analysis of the synthesis to something which could be done in a coffee break then computers would be required which are 10 times as powerful as those available now. This is before the effects of solvents are introduced into the calculation. 

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