Monte Carlo optimization method

Once the retention models have been established by the procedures described above, no further experiments are conducted. The software automatically generates tens of thousands of linear and multi-step gradient profiles in a very short time. It predicts the retention of compounds for each gradient profile generated, evaluates the predicted separation using an optimization function, and searches for the best gradient profile using a super-fast Monte Carlo optimization method [5]. 

Monte Carlo optimization operates in the same way as Monte Carlo simulation." As with the previously discussed GAs, we provide a short overview of MC methods, pointing out key aspects. The Monte Carlo method begins with a random initial point in molecular configuration space (Al, X2, , Ajv). Where the configuration space is understood to include both the three dimensional position of the atoms in the molecule and the identity of the atoms. From Ai, A2,. .Xn new point in configuration space is selected by an elementary Monte Carlo move. This new point is called Xi,triai, X2,triah Xf riai is Set equal to Ai, A2,. .., Ajv with probability... 

A similar algorithm has been used to sample the equilibrium distribution [p,(r )] in the conformational optimization of a tetrapeptide[5] and atomic clusters at low temperature.[6] It was found that when g > 1 the search of conformational space was greatly enhanced over standard Metropolis Monte Carlo methods. In this form, the velocity distribution can be thought to be Maxwellian. 

There are three steps in carrying out any quantum mechanical calculation in HyperChem. First, prepare a molecule with an appropriate starting geometry. Second, choose a calculation method and its associated (Setup menu) options. Third, choose the type of calculation (single point, geometry optimization, molecular dynamics, Langevin dynamics, Monte Carlo, or vibrational analysis) with the relevant (Compute menu) options. 

I Andricioaei, JE Straub. On Monte Carlo and molecular dynamics methods inspired by Tsallis statistics Methodology, optimization, and application to atomic clusters. J Chem Phys 107 9117-9124, 1997. 

To overcome the limitations of the database search methods, conformational search methods were developed [95,96,109]. There are many such methods, exploiting different protein representations, objective function tenns, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method [97], molecular dynamics simulations [92,110,111], genetic algorithms [112], Monte Carlo and simulated annealing [113,114], multiple copy simultaneous search [115-117], self-consistent field optimization [118], and an enumeration based on the graph theory [119]. 

Research looking into tolerance allocation in assembly stacks is by no means new. A current theme is towards an optimization approach using complex routines and/ or cost models (Lin et al., 1997 Jeang, 1995). Advanced methods are also available, such as Monte Carlo Simulation and Method of Moment. ) (Chase and Parkinson, 1991 Wu et al., 1988). The approach presented here is based on empirical process capability measures using simple tolerance models, cost analogies and optimization... 

Ab initio methods allow the nature of active sites to be elucidated and the influence of supports or solvents on the catalytic kinetics to be predicted. Neurock and coworkers have successfully coupled theory with atomic-scale simulations and have tracked the molecular transformations that occur over different surfaces to assess their catalytic activity and selectivity [95-98]. Relevant examples are the Pt-catalyzed NO decomposition and methanol oxidation. In case of NO decomposition, density functional theory calculations and kinetic Monte Carlo simulations substantially helped to optimize the composition of the nanocatalyst by alloying Pt with Au and creating a specific structure of the PtgAu7 particles. In catalytic methanol decomposition the elementary pathways were identified... 

Hpp describes the primary system by a quantum-chemical method. The choice is dictated by the system size and the purpose of the calculation. Two approaches of using a finite computer budget are found If an expensive ab-initio or density functional method is used the number of configurations that can be afforded is limited. Hence, the computationally intensive Hamiltonians are mostly used in geometry optimization (molecular mechanics) problems (see, e. g., [66]). The second approach is to use cheaper and less accurate semi-empirical methods. This is the only choice when many conformations are to be evaluated, i. e., when molecular dynamics or Monte Carlo calculations with meaningful statistical sampling are to be performed. The drawback of semi-empirical methods is that they may be inaccurate to the extent that they produce qualitatively incorrect results, so that their applicability to a given problem has to be established first [67]. 

A new idea has recently been presented that makes use of Monte Carlo simulations [60,61], By defining a range of parameter values, the parameter space can be examined in a random fashion to obtain the best model and associated parameter set to characterize the experimental data. This method avoids difficulties in achieving convergence through an optimization algorithm, which could be a formidable problem for a complex model. Each set of simulated concentration-time data can be evaluated by a goodness-of-fit criterion to determine the models that predict most accurately. 

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