Quantum monte carlo study of the energetics of small hydrogenated and fluoride lithium clusters

An investigation of the energetics of small lithium clusters doped either with a hydrogen or with a fluorine atom as a function of the number of lithium atoms using fixed‐node diffusion quantum Monte Carlo (DMC) simulation is reported. It is found that the binding energy (BE) for the doped clusters increases in absolute values leading to a more stable system than for the pure ones in excellent agreement with available experimental measurements. The BE increases for pure, remains almost constant for hydrogenated, and decreases rapidly toward the bulk lithium for the fluoride as a function of the number of lithium atoms in the clusters. The BE, dissociation energy as well as the second difference in energy display a pronounced odd–even oscillation with the number of lithium atoms. The electron correlation inverts the odd–even oscillation pattern for the doped in comparison with the pure clusters and has an impact of 29%–83% to the BE being higher in the pure cluster followed by the hydrogenated and then by the fluoride. The dissociation energy and the second difference in energy indicate that the doped cluster Li₃H is the most stable whereas among the pure ones the more stable are Li₂, Li₄, and Li₆. The electron correlation energy is crucial for the stabilization of Li₃H. © 2016 Wiley Periodicals, Inc.     

Effective protein conformational sampling based on predicted torsion angles

Protein structure prediction is a long‐standing problem in molecular biology. Due to lack of an accurate energy function, it is often difficult to know whether the sampling algorithm or the energy function is the most important factor for failure of locating near‐native conformations of proteins. This article examines the size dependence of sampling effectiveness by using a perfect “energy function”: the root‐mean‐squared distance from the target native structure. Using protein targets up to 460 residues from critical assessment of structure prediction techniques (CASP11, 2014), we show that the accuracy of near native structures sampled is relatively independent of protein sizes but strongly depends on the errors of predicted torsion angles. Even with 40% out‐of‐range angle prediction, 2 Å or less near‐native conformation can be sampled. The result supports that the poor energy function is one of the bottlenecks of structure prediction and predicted torsion angles are useful for overcoming the bottleneck by restricting the sampling space in the absence of a perfect energy function. © 2015 Wiley Periodicals, Inc.     

Fullrmc,a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence

A new Reverse Monte Carlo (RMC) package “fullrmc” for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.     

Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM

Protein‐ligand docking is a commonly used method for lead identification and refinement. While traditional structure‐based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well‐appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid‐based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced‐fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re‐docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs. © 2015 Wiley Periodicals, Inc.     

Confinement Induced Ordering in Fluid of Hard Ellipsoids

The ordering configurations of a fluid of anisotropic ellipsoids under the confinement of two apposing impenetrable walls are studied by Monte Carlo simulations. The excess adsorption of the fluid on the walls with respect to the aspect ratio has a maximum at the critical aspect ratio of 2.9 in high-density ellipsoid fluids, indicating an orientational ordering in the adjacent region of the walls, which is confirmed by probing into the density configurations and the orientational order parameter in the adjacent region of the walls for varying aspect ratios. In addition, the orientational order parameter in the bulk fluid at the same density is calculated, and it indicates an isotropic state as the bulk density is still below the bulk isotropic-to-nematic transition. Therefore, it can be concluded that the anisotropic ordering near the walls in the ellipsoid fluid that exhibits isotropic in the bulk is induced by the confinement effect of the walls.     

删失截断情形下失效率变点模型的Bayes参数估计

通过添加部分缺失寿命变量数据,得到了删失截断情形下失效率变点模型相对简单的似然函数.讨论了所添加缺失数据变量的概率分布和随机抽样方法.利用Monte Carlo EM算法对未知参数进行了迭代.结合Metropolis-Hastings算法对参数的满条件分布进行了Gibbs抽样,基于Gibbs样本对参数进行估计,详细介绍了MCMC方法的实施步骤.随机模拟试验的结果表明各参数Bayes估计的精度较高.     

超支化聚合反应的多尺度模拟

超支化聚合物具有与树枝状大分子相似的物理和化学性质,其具有合成简单、分子量分布宽等突出特点,超支化聚合物分子的结构形成取决于聚合反应过程,本文介绍了超支化聚合反应模拟研究的最新进展.首先介绍了八位置键涨落粗粒化格子模型在超支化聚合反应模拟中的应用,该方法考虑了聚合物分子空间位阻效应、分子内成环和反应点活性等影响因素,从而可以模拟不同类型的超支化聚合反应;为了定量描述单体和聚合物分子结构,研究者进一步发展了杂化多尺度超支化聚合反应模拟方法,该方法通过玻尔兹曼反演迭代方法获取单体和聚合物特异性粗粒化力场,然后通过粗粒化分子动力学方法结合反应性Monte Carlo方法对特异性超支化聚合反应进行定量模拟.多尺度聚合反应模拟不仅可以精确计算超支化聚合物分子量、多分散性指数和支化度等一般性聚合物参数,还可以获取分子成环率、超支化大分子构象等重要分子结构信息,在超支化聚合反应基础研究与预测方面具有重要应用价值.     

Combination of sorption properties of polydimethylsiloxane and solid‐phase extraction sorbents in a single composite material for the passive sampling of polar and apolar pesticides in water

Passive sampling techniques have been developed as an alternative method for in situ integrative monitoring of trace levels of neutral pesticides in environmental waters. The objective of this work was to develop a new receiving phase for pesticides with a wide range of polarities in a single step. We describe the development of three new composite silicone rubbers, combining polydimethylsiloxane mechanical and sorption properties with solid‐phase extraction sorbents, prepared as a receiving phase for passive sampling. A composite silicone rubber composed of polydimethylsiloxane/poly(divinylbenzene‐co‐N‐vinylpyrrolidone) was selected by batch experiments for its high sorption properties for pesticides with octanol‐water partition coefficients ranging from 2.3 to 5.5. We named this composite material “Polar/Apolar Composite Silicone Rubber”. A structural study by scanning electron microscopy confirmed the homogeneous dispersion of the sorbent particles and the encapsulation of particles within the polydimethylsiloxane matrix. We also demonstrate that this composite material is resistant to common solvents used for the back‐extraction of analytes and has a maximal resistance temperature of 350°C. Therefore, the characteristics of the “Polar/Apolar Composite Silicone Rubber” meet most of the criteria for use as a receiving phase for the passive sampling of pesticides.     

A micro-level claim count model with overdispersion and reporting delays

The accurate estimation of outstanding liabilities of an insurance company is an essential task. This is to meet regulatory requirements, but also to achieve efficient internal capital management. Over the recent years, there has been increasing interest in the utilisation of insurance data at a more granular level, and to model claims using stochastic processes. So far, this so-called ‘micro-level reserving’ approach has mainly focused on the Poisson process.In this paper, we propose and apply a Cox process approach to model the arrival process and reporting pattern of insurance claims. This allows for over-dispersion and serial dependency in claim counts, which are typical features in real data. We explicitly consider risk exposure and reporting delays, and show how to use our model to predict the numbers of Incurred-But-Not-Reported (IBNR) claims. The model is calibrated and illustrated using real data from the AUSI data set.