File Name: monte carlo and molecular dynamics simulations in polymer science .zip
Molecular Dynamics in Membranes
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Contact Newsletter. Newsletter Sign in for news and special offers information. On-line access. Your basket. Written by leading experts from around the world, Monte Carlo and Molecular Dynamics Simulations in Polymer Science comprehensively reviews the latest simulation techniques for macromolecular materials. Focusing in particular on numerous new techniques, the book offers authoritative introductions to solutions of neutral polymers and polyelectrolytes; dynamics of polymer melts, rubbers and gels, and glassy materials; thermodynamics of polymer mixing and mesophase formation, and polymers confined at interfaces and grafted to walls.
Monte Carlo method
There exists a broad class of sequencing problems in soft materials such as proteins and polymers that can be formulated as a heuristic search that involves decision making akin to a computer game. AI gaming algorithms such as Monte Carlo tree search MCTS gained prominence after their exemplary performance in the computer Go game and are decision trees aimed at identifying the path moves that should be taken by the policy to reach the final winning or optimal solution. Major challenges in inverse sequencing problems are that the materials search space is extremely vast and property evaluation for each sequence is computationally demanding. Reaching an optimal solution by minimizing the total number of evaluations in a given design cycle is therefore highly desirable. We demonstrate that one can adopt this approach for solving the sequencing problem by developing and growing a decision tree, where each node in the tree is a candidate sequence whose fitness is directly evaluated by molecular simulations. We interface MCTS with MD simulations and use a representative example of designing a copolymer compatibilizer, where the goal is to identify sequence specific copolymers that lead to zero interfacial energy between two immiscible homopolymers. If you are not the author of this article and you wish to reproduce material from it in a third party non-RSC publication you must formally request permission using Copyright Clearance Center.
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Box , Eindhoven, The Netherlands. We review recent results from computer simulation studies of polymer glasses, from the chain dynamics around the glass transition temperature T g to the mechanical behaviour below T g. These results clearly show that modern computer simulations are able to address and give clear answers to some important issues in the field, in spite of the obvious limitations in terms of length and time scales. In the present review we discuss the cooling rate effects, and the dynamic slowing down of different relaxation processes when approaching T g for both model and chemistry-specific polymer glasses. The impact of geometric confinement on the glass transition is discussed in detail.
Bulatov, "Core energies of dislocations in bcc metals", Physical Review Materials , in press , Christopher B. Akhondzadeh, Ryan B.
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Chain architecture effect on static and dynamic properties of unentangled polymers is explored by molecular dynamics simulation and Rouse mode analysis based on graph theory. For rings, unlike open chains they are compact in term of global sizes. Due to EV effect and nonconcatenated constraints their local structure exhibits a quite different non-Gaussian behavior from open chains, i. Deviation from ideality is further evidenced by limited applicability of Rouse prediction to mode amplitude and relaxation time at high modes as well as the non-constant and mode-dependent scaled Rouse mode amplitudes, while the latter is architecture-dependent and even molecular weight dependent for rings.
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Monte Carlo methods , or Monte Carlo experiments , are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes:  optimization , numerical integration , and generating draws from a probability distribution.
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We adapt this method to molecular dynamics simulations and demonstrate its excellent accelerating effect by simulating the folding of a short peptide commonly used to gauge the performance of algorithms. The method is compared to the well established parallel tempering approach and is found to yield similar performance for the same computational resources. In contrast to other methods, however, population annealing scales to a nearly arbitrary number of parallel processors, and it is thus a unique tool that enables molecular dynamics to tap into the massively parallel computing power available in supercomputers that is so much needed for a range of difficult computational problems.
The bond-fluctuation model of polymer chains has been used to study layers of end-grafted polymers anchoring at repulsive walls for a broad range of chain length, grafting densities and solvent quality. The dynamics of monomers and associated relaxation times are investigated and interpreted by phenomenological theories and scaling arguments. The case is also considered where a chain is cut off from its anchor point and the chain is subsequently expelled from the brush.