On the study of nonlinear dynamics of complex chemical reaction systems

When driven far from equilibrium,nonlinear chemical reactions often show a variety of self-organization behavior,including chemical oscillations,waves,chaos and patterns[1].Recently,the study of such nonlinear phenomena in‘complex’systems,such as the li…     

Data modelling with neural networks: Advantages and limitations

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.     

Study of Nonlinear Resonance Effect in Paul Trap

In this article, we investigated the nonlinear resonance effect in the Paul trap with a superimposed hexapole field, which was assumed as a perturbation to the quadrupole field. On the basis of the Poincare-Lighthill-Kuo (PLK) perturbation method, ion motional equation, known as nonlinear Mathieu equation (NME) was expressed as the addition of approximation equations in terms of perturbation order. We discussed the frequency characteristics of ion axial-radial (z-r) coupled motion in the nonlinear field, derived the expressions of ion trajectories and nonlinear resonance conditions, and found that the mechanism of nonlinear resonance is similar to the normal resonance. The frequency spectrum of ion motion in nonlinear field includes not only the natural frequency series but also nonlinear introduced frequency series, which provide the driving force for the nonlinear resonance. The nonlinear field and the nonlinear effects are inevitable in practical ion trap experiments. Our method provides better understanding of these nonlinear effects and would be helpful for the instrumentation for ion trap mass spectrometers.      

Asymmetric autocatalysis: product recruitment for the increase in the chiral environment (PRICE)

Asymmetric catalytic reactions are possible via efficient transfer of the chiral environment of a reaction to the transition state. In theory any asymmetric structure may contribute to this, including the product of the reaction itself. For product influence to be significant, a nonlinear effect needs to operate, whereby one diastereomer of the product/catalyst assists the reaction, and the other does not. When these conditions are satisfied, we obtain an asymmetric autocatalytic reaction in which the enantiomeric excess of a compound (that is both product and catalyst) actually increases as the reaction iterates. It is only recently that we have seen reports of such processes. Of particular interest are Soai's reports of the alkylation of aromatic heterocycles. Such reactions, aside from their inherent interest, may offer clues into the origins of asymmetric molecular replication that predated the origin of life.     

Modelling the crystalline deformation of native and regenerated cellulose

The molecular mechanics modelling of the deformation of a number of proposed structures for the crystalline regions of cellulose I- , I- and II are reported. The structures used are from coordinates that have recently been reported, which have made particular reference to the ability to locate the positions of hydrogen bonds. By comparison to previously reported structures of cellulose, where the emphasis on this has also been made, and in a diagnostic way, it is shown that it is possible to make some conclusions as to their validity. The effect of removing the intramolecular hydrogen bonding is also reported. All structures, with one exception, are shown to be sensitive to this operation. Two approaches to the molecular mechanics modelling are reported, wherein the structures are minimised under restraint, by altering the c-spacing, within the COMPASS^TM forcefield to obtain a simple chain stiffness value or alternatively by performing a full elastic constants determination for the unit cell.     

Machine learning modelling of chemical reaction characteristics: yesterday,today, tomorrow

The synthesis of the desired chemical compound is the main task of synthetic organic chemistry. The predictions of reaction conditions and some important quantitative characteristics of chemical reactions as yield and reaction rate can substantially help in the development of optimal synthetic routes and assessment of synthesis cost. Theoretical assessment of these parameters can be performed with the help of modern machine-learning approaches, which use available experimental data to develop predictive models called quantitative or qualitative structure–reactivity relationship (QSRR) modelling. In the article, we review the state-of-the-art in the QSRR area and give our opinion on emerging trends in this field.     

Molecular origins of the remarkable chiral sensitivity of second-order nonlinear optics.

Recent observations of remarkably large chiroptical effects in second-harmonic generation (SHG) and sum-frequency generation (SFG) measurements suggest exciting possibilities for the development of new chiral-specific spectroscopies and novel chiral materials for nonlinear optics. Several fundamental studies designed to elucidate the molecular and macromolecular origins of the chiral responses are reviewed to provide a framework for development of this emerging field. In general, the chiral activity in SHG and SFG has the potential to arise from complex interactions between hosts of different competing effects. Fortunately, relatively simple electric dipole-allowed mechanisms routinely dominate the nonlinear optical chiral activities of most practical systemsexpressions can often be generated to link the. This substantial reduction in complexity allows for the development of simple models connecting the macroscopic nonlinear optical response to intuitive molecular and supramolecular properties.     

Modeling robust QSAR

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.     

Theoretical calculations of physico-chemical and spectroscopic properties of bioinorganic systems: current limits and perspectives

In the last decade, we have witnessed substantial progress in the development of quantum chemical methodologies. Simultaneously, robust solvation models and various combined quantum and molecular mechanical (QM/MM) approaches have become an integral part of quantum chemical programs. Along with the steady growth of computer power and, more importantly, the dramatic increase of the computer performance to price ratio, this has led to a situation where computational chemistry, when exercised with the proper amount of diligence and expertise, reproduces, predicts, and complements the experimental data. In this perspective, we review some of the latest achievements in the field of theoretical (quantum) bioinorganic chemistry, concentrating mostly on accurate calculations of the spectroscopic and physico-chemical properties of open-shell bioinorganic systems by wave-function (ab initio) and DFT methods. In our opinion, the one-to-one mapping between the calculated properties and individual molecular structures represents a major advantage of quantum chemical modelling since this type of information is very difficult to obtain experimentally. Once (and only once) the physico-chemical, thermodynamic and spectroscopic properties of complex bioinorganic systems are quantitatively reproduced by theoretical calculations may we consider the outcome of theoretical modelling, such as reaction profiles and the various decompositions of the calculated parameters into individual spatial or physical contributions, to be reliable. In an ideal situation, agreement between theory and experiment may imply that the practical problem at hand, such as the reaction mechanism of the studied metalloprotein, can be considered as essentially solved.     

Electric-field-induced patterns in thin polymer films: weakly nonlinear and fully nonlinear evolution

A thin polymer melt on a substrate can be unstable to an electric field normal to the interface, a phenomenon that can be harnessed as a patterning technique with a range of potential applications. Motivated by the variety of patterns observed in experiments for polymers under both unpatterned and patterned masks, we describe here, from theoretical and numerical analyses, how nonlinear effects govern the growth of the instability and determine the final patterns. In particular, we discuss the nonlinear growth in terms of interactions among different Fourier modes and show that the second- and third-order nonlinearities favor the growth of hexagonal patterns under a featureless mask, in agreement with experimental observations. Also, numerical simulations based on the fully nonlinear model validate the prediction of the weakly nonlinear analysis: hexagonal patterns do emerge under an unpatterned mask. Furthermore, in one-dimensional simulations, we demonstrate the energetic evolution of this patterning process and reveal several "kinetically stable structures" along the path to the thermodynamically stable state. Two-dimensional simulations allow us to study the effects of both mask patterns and the initial film thickness. Generally, patterns on the mask guide the growth such that the pattern conforms to the geometric shapes. Interestingly, a small cylindrical protrusion at the center of the mask can produce exactly the same pattern as a large, flat, circular protrusion. The initial film thickness or the thickness ratio of the polymer layer to the air gap plays an important role in determining the final pattern formed. Finally, we demonstrate, by two simple examples, that the simulations can provide insights on "smart" mask designs for producing large areas of well-ordered patterns.     

Dissipaton Equation of Motion Theory versus Fokker-Planck Quantum Master Equation

The quest of exact and nonperturbative methods on quantum dissipation with nonlinear coupling environments remains in general a great challenge. In this review we present a comprehensive account on two approaches to the entangled system-and-environment dynamics, in the presence of linear-plus-quadratic coupling bath. One is the dissipaton-equation-ofmotion (DEOM) theory that has been extended recently to treat the nonlinear coupling environment. Another is the extended Fokker-Planck quantum master equation (FP-QME) approach that will be constructed in this work, based on its DEOM correspondence. We closely compare these two approaches, with the focus on the underlying quasi-particle picture, physical implications, and implementations.     

Linear-scaling soft-core scheme for alchemical free energy calculations

Alchemical free energy calculations involving the removal or insertion of atoms into condensed phase systems generally make use of soft-core scaling of nonbonded interactions, designed to circumvent numerical instabilities that arise from weakly interacting "hard" atoms in close proximity. Current methods model soft-core atoms by introducing a nonlinear dependence between the shape of the interaction potential and the strength of the interaction. In this article, we propose a soft-core method that avoids introducing such a nonlinear dependence, through the application of a smooth flattening of the potential energy only in a region that is energetically accessible under normal conditions. We discuss the benefits that this entails and explore a selection of applications, including enhanced methods for the estimation of free energy differences and for the automated optimization of the placement of intermediate states in multistage alchemical calculations.     

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.     

Mathematical modelling of percutaneous absorption

This review examines recent progress made in the field of modelling and predicting percutaneous absorption. It describes initial qualitative modelling and how quantitative approaches were pioneered and then developed, particularly in the context of the analysis of specific subsets of data. It then focuses on recent developments, including non-linear modelling and discusses recommendations in model construction, development and validation, suggesting that some models do not fit proposed guidelines.     

Understanding prebiotic chemistry through the analysis of extraterrestrial amino acids and nucleobases in meteorites

The discoveries of amino acids of extraterrestrial origin in many meteorites over the last 50 years have revolutionized the Astrobiology field. A variety of non-terrestrial amino acids similar to those found in life on Earth have been detected in meteorites. A few amino acids have even been found with chiral excesses, suggesting that meteorites could have contributed to the origin of homochirality in life on Earth. In addition to amino acids, which have been productively studied for years, sugar-like molecules, activated phosphates, and nucleobases have also been determined to be indigenous to numerous meteorites. Because these molecules are essential for life as we know it, and meteorites have been delivering them to the Earth since accretion, it is plausible that the origin(s) of life on Earth were aided by extraterrestrially-synthesized molecules. Understanding the origins of life on Earth guides our search for life elsewhere, helping to answer the question of whether biology is unique to Earth. This tutorial review focuses on meteoritic amino acids and nucleobases, exploring modern analytical methods and possible formation mechanisms. We will also discuss the unique window that meteorites provide into the chemistry that preceded life on Earth, a chemical record we do not have access to on Earth due to geologic recycling of rocks and the pervasiveness of biology across the planet. Finally, we will address the future of meteorite research, including asteroid sample return missions.     

遗传算法与遗传编程的联用

遗传算法 ( Genetic algorithm)是一种概率搜索算法 [1] ,它的搜索过程与自然界生物进化过程相似[2 ] ,在搜索过程中不易陷入局部最优 .遗传编程 ( Genetic programming)是近年来随着程序设计自动化而产生和发展起来的一种自动编程技术 [3] .它继承了遗传算法的基本思想 ,是一种更广泛的适应系统方法 [4 ] .本文将遗传算法和遗传编程相结合 ,构造了既有广泛的搜索能力 ,又有很强的局部精化能力的 GA- GP算法 ,充分利用了遗传算法局部精化能力强、遗传编程适应面广的优点 .该算法应用范围广 ,可应用于多种非线性函数式及分式关系的建模 .1…     

Nonlinear mapping networks

Among the many dimensionality reduction techniques that have appeared in the statistical literature, multidimensional scaling and nonlinear mapping are unique for their conceptual simplicity and ability to reproduce the topology and structure of the data space in a faithful and unbiased manner. However, a major shortcoming of these methods is their quadratic dependence on the number of objects scaled, which imposes severe limitations on the size of data sets that can be effectively manipulated. Here we describe a novel approach that combines conventional nonlinear mapping techniques with feed-forward neural networks, and allows the processing of data sets orders of magnitude larger than those accessible with conventional methodologies. Rooted on the principle of probability sampling, the method employs a classical algorithm to project a small random sample, and then "learns" the underlying nonlinear transform using a multilayer neural network trained with the back-propagation algorithm. Once trained, the neural network can be used in a feed-forward manner to project the remaining members of the population as well as new, unseen samples with minimal distortion. Using examples from the fields of image processing and combinatorial chemistry, we demonstrate that this method can generate projections that are virtually indistinguishable from those derived by conventional approaches. The ability to encode the nonlinear transform in the form of a neural network makes nonlinear mapping applicable to a wide variety of data mining applications involving very large data sets that are otherwise computationally intractable.