Monte Carlo search method

Monte Carlo search methods are stochastic techniques based on the use of random numbers and probability statistics to sample conformational space. The name Monte Carlo was originally coined by Metropolis and Ulam [4] during the Manhattan Project of World War II because of the similarity of this simulation technique to games of chance. Today a variety of Monte Carlo (MC) simulation methods are routinely used in diverse fields such as atmospheric studies, nuclear physics, traffic flow, and, of course, biochemistry and biophysics. In this section we focus on the application of the Monte Carlo method for... 

In the random (or stochastic or Monte Carlo) search method one starts with a stable conformer and generates new initial configurations either by randomly chang-... 

Scanning of a potential energy surface (see potential energy surface). The methods currently used include random search (stochastic search, e. g., Monte Carlo methods) and molecular dynamics (see deterministic search, Monte Carlo search, stochastic search, molecular dynamics, scanning an energy surface). 

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]. 

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