自由基聚合反应的Monte Carlo模拟方法

本文将化学反应动力学的MonteCarlo模拟方法运用到引发剂引发的自由基聚合反应的非稳态动力学,针对自由基聚合反应动力学数值模拟所特有的"无伸缩问题",采用"偏倚抽样法"解决了MonteCarlo模拟中的"无伸缩问题",模拟结果与非稳态动力学解的结果完全一致,此算法易推广到研究更复杂的自由基聚合反应体系。     

Importance sampling for quantum Monte Carlo in manifolds: addressing the time scale problem in simulations of molecular aggregates

Several importance sampling strategies are developed and tested for stereographic projection diffusion Monte Carlo in manifolds. We test a family of one parameter trial wavefunctions for variational Monte Carlo in stereographically projected manifolds which can be used to produce importance sampling. We use the double well potential in one dimensional Euclidean space to study systematically sampling issues for diffusion Monte Carlo. We find that diffusion Monte Carlo with importance sampling in manifolds is orders of magnitude more efficient compared to unguided diffusion Monte Carlo. Additionally, diffusion Monte Carlo with importance sampling in manifolds can overcome problems with nonconfining potentials and can suppress quasiergodicity effectively. We obtain the ground state energy and the wavefunction for the Stokmayer trimer.     

Monte Carlo methods for small molecule high-throughput experimentation

By analogy with Monte Carlo algorithms, we propose new strategies for design and redesign of small molecule libraries in high-throughput experimentation, or combinatorial chemistry. Several Monte Carlo methods are examined, including Metropolis, three types of biased schemes, and composite moves that include swapping or parallel tempering. Among them, the biased Monte Carlo schemes exhibit particularly high efficiency in locating optimal compounds. The Monte Carlo strategies are compared to a genetic algorithm approach. Although the best compounds identified by the genetic algorithm are comparable to those from the better Monte Carlo schemes, the diversity of favorable compounds identified is reduced by roughly 60%.     

Quantum Monte Carlo calculations of the potential energy curve of the helium dimer

We report results of two quantum Monte Carlo methods -- variational Monte Carlo and diffusion Monte Carlo -- on the potential energy curve of the helium dimer. In contrast to previous quantum Monte Carlo calculations on this system, we have employed trial wave functions of the Slater-Jastrow form and used the fixed node approximation for the fermion nodal surface. We find both methods to be in excellent agreement with the best theoretical results at short range. In addition, the diffusion Monte Carlo results give very good agreement across the whole potential energy curve, while the Slater-Jastrow wave function fails to bind the dimer at all.     

Theory and algorithms for mixed Monte Carlo-stochastic dynamics simulations

The recently introduced mixed MC-SD method is a fundamentally new procedure which essentially eliminates the distinction between Monte Carlo and dynamics. Unlike other methods which utilize forces, Brownian motion or dynamical steps to generate new trial configurations in a Monte Carlo search, mixed MC-SD does stochastic dynamics on the cartesian space of a molecule and Monte Carlo on the torsion space of the molecule simultaneously. After each dynamical step, a random deformation of a rotatable torsion is performed and accepted or rejected according to the Metropolis criteria. The next dynamical step is performed from the most recent configuration and the velocities from the previous dynamical step. The smooth merging of Monte Carlo and dynamics requires the use of the stochastic velocity Verlet integration scheme. Here, the velocity Verlet stochastic dynamics method is derived, and the reasons why it can be joined with Metropolis Monte Carlo in a continuous fashion are explored.     

New algorithm in the basin hopping Monte Carlo to find the global minimum structure of unary and binary metallic nanoclusters

The basin-hopping Monte Carlo algorithm was modified to more effectively determine a global minimum structure in pure and binary metallic nanoclusters. For a pure metallic Ag55 nanocluster, the newly developed quadratic basin-hopping Monte Carlo algorithm is 3.8 times more efficient than the standard basin-hopping Monte Carlo algorithm. For a bimetallic Ag42Pd13 nanocluster, the new algorithm succeeds in finding the global minimum structure by 18.3% even though the standard basin-hopping Monte Carlo algorithm fails to achieve it.     

Hybrid Monte Carlo implementation of the Fourier path integral algorithm

This paper formulates a hybrid Monte Carlo implementation of the Fourier path integral (FPI-HMC) approach with partial averaging. Such a hybrid Monte Carlo approach allows one to generate collective moves through configuration space using molecular dynamics while retaining the computational advantages associated with the Fourier path integral Monte Carlo method. In comparison with the earlier Metropolis Monte Carlo implementations of the FPI algorithm, the present HMC method is shown to be significantly more efficient for quantum Lennard-Jones solids and suggests that such algorithms may prove useful for efficient simulations of a range of atomic and molecular systems.     

Flexible polyelectrolyte simulations at the Poisson-Boltzmann level: a comparison of the kink-jump and multigrid configurational-bias Monte Carlo methods

We present a new approach for simulating the motions of flexible polyelectrolyte chains based on the continuous kink-jump Monte Carlo technique coupled to a lattice field theory based calculation of the Poisson-Boltzmann (PB) electrostatic free energy "on the fly." This approach is compared to the configurational-bias Monte Carlo technique, in which the chains are grown on a lattice and the PB equation is solved for each configuration with a linear scaling multigrid method to obtain the many-body free energy. The two approaches are used to calculate end-to-end distances of charged polymer chains in solutions with varying ionic strengths and give similar numerical results. The configurational-bias Monte Carlo/multigrid PB method is found to be more efficient, while the kink-jump Monte Carlo method shows potential utility for simulating nonequilibrium polyelectrolyte dynamics.     

Combining reactive and configurational-bias Monte Carlo: confinement influence on the propene metathesis reaction system in various zeolites

In order to efficiently calculate chemical equilibria of large molecules in a confined environment the reactive Monte Carlo technique is combined with the configurational-bias Monte Carlo approach. To prove that detailed balance is fulfilled the acceptance rule for this combination of particular Monte Carlo techniques is derived in detail. Notably, by using this derivation all other acceptance rules of any Monte Carlo trial moves usually carried out in combination with the configurational-bias Monte Carlo approach can be deduced from it. As an application of the combination of reactive and configurational-bias Monte Carlo the influence of different zeolitic confinements (MFI, TON, LTL, and FER) on the reaction equilibrium and the selectivity of the propene metathesis reaction system was investigated. Compared to the bulk phase the conversion is increased significantly. The authors study this reaction system in the temperature range between 300 and 600 K, and the pressure range from 1 to 7 bars. In contrast to the bulk phase, pressure and temperature have a strong influence on the composition of the reaction mixture in confinement. At low pressures and temperatures both conversion and selectivity are highest. Furthermore, the equilibrium composition is strongly dependent on the type of zeolite. This demonstrates the important role of the host structure in catalytic systems.     

Monte Carlo methods for design and analysis of radiation detectors

An overview of Monte Carlo as a practical method for designing and analyzing radiation detectors is provided. The emphasis is on detectors for radiation that is either directly or indirectly ionizing. This overview paper reviews some of the fundamental aspects of Monte Carlo, briefly addresses simulation of radiation transport by the Monte Carlo method, discusses the differences between direct and inverse detection problems, and illustrates how various Monte Carlo methods can be used in design and analysis of radiation detectors.     

Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo

An efficient exploration of the configuration space of a biopolymer is essential for its structure modeling and prediction. In this study, the authors propose a new Monte Carlo method, fragment regrowth via energy-guided sequential sampling (FRESS), which incorporates the idea of multigrid Monte Carlo into the framework of configurational-bias Monte Carlo and is suitable for chain polymer simulations. As a by-product, the authors also found a novel extension of the Metropolis Monte Carlo framework applicable to all Monte Carlo computations. They tested FRESS on hydrophobic-hydrophilic (HP) protein folding models in both two and three dimensions. For the benchmark sequences, FRESS not only found all the minimum energies obtained by previous studies with substantially less computation time but also found new lower energies for all the three-dimensional HP models with sequence length longer than 80 residues.     

Correlation method for variance reduction of Monte Carlo integration in RS-HDMR

The High Dimensional Model Representation (HDMR) technique is a procedure for efficiently representing high-dimensional functions. A practical form of the technique, RS-HDMR, is based on randomly sampling the overall function and utilizing orthonormal polynomial expansions. The determination of expansion coefficients employs Monte Carlo integration, which controls the accuracy of RS-HDMR expansions. In this article, a correlation method is used to reduce the Monte Carlo integration error. The determination of the expansion coefficients becomes an iteration procedure, and the resultant RS-HDMR expansion has much better accuracy than that achieved by direct Monte Carlo integration. For an illustration in four dimensions a few hundred random samples are sufficient to construct an RS-HDMR expansion by the correlation method with an accuracy comparable to that obtained by direct Monte Carlo integration with thousands of samples.     

Quantum Monte Carlo for electronic excitations of free-base porphyrin

Accurate calculations of allowed and nonallowed transitions in porphyrin are reported. Using the quantum Monte Carlo method in the diffusion Monte Carlo variant, the vertical transition between the ground state singlet and the second excited state singlet as well as the adiabatic transition between the ground state and the lowest triplet state have been computed for this 162-electron system. The present theoretical results are compared to experiment and to results of other theoretical methods. The diffusion Monte Carlo energy differences are found to be in excellent agreement with experiment.     

Approaching chemical accuracy with quantum Monte Carlo

A quantum Monte Carlo study of the atomization energies for the G2 set of molecules is presented. Basis size dependence of diffusion Monte Carlo atomization energies is studied with a single determinant Slater-Jastrow trial wavefunction formed from Hartree-Fock orbitals. With the largest basis set, the mean absolute deviation from experimental atomization energies for the G2 set is 3.0 kcal/mol. Optimizing the orbitals within variational Monte Carlo improves the agreement between diffusion Monte Carlo and experiment, reducing the mean absolute deviation to 2.1 kcal/mol. Moving beyond a single determinant Slater-Jastrow trial wavefunction, diffusion Monte Carlo with a small complete active space Slater-Jastrow trial wavefunction results in near chemical accuracy. In this case, the mean absolute deviation from experimental atomization energies is 1.2 kcal/mol. It is shown from calculations on systems containing phosphorus that the accuracy can be further improved by employing a larger active space.     

Density distributions in the water shell of myoglobin

Two different approaches were used to explain the distribution of water coordinates in the crystallographically invisible part of the unit cell. Monte Carlo calculations were done starting from different initial water structures. Extended Monte Carlo calculations using equal initial structures were also used to obtain two further water structures. The differences between the Monte Carlo water structures were used to calculate the mean-square displacements of the water molecules. Monte Carlo calculations starting from different structures lead to a mean-square displacement of 0.58 Ų, whereas extended Monte Carlo moves using the same initial structure only show a mean-square displacement of 0.17 Ų. The mean-square displacement of 0.58 Ų can be used to explain the experimental data. © 1996 John Wiley & Sons, Inc.     

基于自适应蒙特卡洛法评定测量不确定度程序开发与应用

进行了基于自适应蒙特卡洛法评定测量不确定度的程序开发与应用。基于Python语言,设计开发自适应蒙特卡洛法评定测量不确定度程序,包含评定过程框架、自定义变量名称模块、过程参数关联计算模块以及蒙特卡洛法采样计算模块。程序界面简洁,操作简单,计算准确,适用于任意多个独立变量、任意多个过程参数及单一被测量的数学模型,为利用自适应蒙特卡洛法评定测量不确定度提供了方便。     

Theoretical advances in the dissolution studies of mineral–water interfaces

This article presents theoretical advances in computational modeling of dissolution at mineral–water interfaces with specific emphasis on silicates. Two different Monte Carlo methods have been developed that target equilibrium properties and kinetics in silicate–water dissolution. The equilibrium properties are explored using the combined reactive Monte Carlo and configurational bias Monte Carlo (RxMC-CBMC) method. The new RxMC-CBMC method is designed to affordably simulate the three-dimensional structure of the mineral with explicit water molecules. The kinetics of the overall dissolution process is studied using a stochastic kinetic Monte Carlo method that utilizes rate constants obtained from accurate ab initio calculations. Both these methods provide important complementary perspective of the complex dynamics involving chemical and physical interactions at the mineral–water interface. The results are compared to experimental and previous computational data available in the literature.     

Free energies and phase equilibria of chain molecules

In this Article, a review is presented of recent developments in Monte Carlo simulations of chain molecules. The Rosenbluth chain insertion technique is used to calculate the free energy of the chain molecules. Furthermore, this insertion method is used to generate biased Monte Carlo moves. It is shown that this bias can be removed by adjusting the acceptance rules such that configurations are generated with their correct Boltzmann weight. This configurational-bias Monte Carlo method can be combined with the Gibbs-ensemble technique which results in an efficient method to simulate phase equilibria of chain molecules.     

A diffusion quantum Monte Carlo study of geometries and harmonic frequencies of molecules

This article describes an approach in determination of equilibrium geometries and harmonic frequencies of molecules by the Ornstein-Uhlenbeck diffusion quantum Monte Carlo method based on the floating spherical Gaussians. In conjunction with a projected and renormalized Hellmann-Feynman gradient and an electronic energy at variational Monte Carlo and diffusion quantum Monte Carlo, respectively, the quasi-Newton algorithm implemented with the Broyden-Fletcher-Goldfarb-Shanno updated Hessian was used to find the optimized molecular geometry. We applied this approach to N2 and H2O molecules. The geometry and harmonic frequencies calculated were consistent with some sophisticated ab initio calculated values within reasonable statistical uncertainty.     

O—酰基—α—酮肟光分解反应的Monte Carlo处理

本文首次对光化学反应体系用Monte Carlo方法进行模拟处理。通过5个O-酰基-α-酮肟光分解反应的Monte Carlo模拟,可避免解析解中由于对吸收光强须采用一级近似求解动力学微分方程组,而造成拟合反应在后期产生与实验结果的偏差。