Computers & chemistry最新文献

The complex relationship between maximum absorption wavelength (λ_max), molar absorptivity (ε) of the coordination compounds formed from m-acetyl-chlorophosphonazo (CPA-mA) and the metal ions, the acidity of coordination reaction, some properties of metal ions and the properties of more than 20 coordination compounds were studied using artificial neural networks with extended delta-bar-delta EDBD back learning algorithms in this paper. Six parameters: the pH of coordination reactions, metal ion radius (R), relative atomic weight (Wt), ionic electronic energy (E), metal ion standard Gibbs’ free energy (ΔG⁰) and hard–soft acid–base dual scale (f) were used as input parameters, to predict the λ_max and ε of the coordination compounds. The structures of networks and the learning times were optimized. The best networks structure is 6–7–2. The optimum number of learning times is about 160 196. It is shown that the maximum relative error is no more than 6% in the testing set. The trained networks are used to simulate the complicated relations between the metal ion properties, coordination reaction conditions and the properties of coordination compounds. This optimized networks have been used for the prediction of the λ_max and ε of coordination compounds formed from Tb³⁺, Ho³⁺ with CPA-mA separately and with satisfactory results.

An algorithm is described by means of which the Kekulé structures of a catacondensed benzenoid molecule (with h hexagons) are transformed into binary codes (of length h). By this, computer-aided manipulations with, and memory-storage of Kekulé structures are much facilitated. Any Kekulé structure can easily be recovered from its binary code.

Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.

General expressions are derived for the hyper-Wiener index (WW) for several series of hydrocarbons, both benzenoid and non-benzenoid, including some two-dimensional networks. Indeed, such expressions were found for all but one series studied. Methods are also described for eliminating the register overflow problem that besets computer-based approaches to calculating WW for large structures by means of the distance matrix.

Considering that the two-dimensional (2D) feature map of the high-dimensional chemical patterns can more concisely and efficiently represent the pattern characteristic, a new procedure integrating self-organizing map (SOM) networks with correlative component analysis (CCA) is proposed. Firstly, CCA was used to identify the most important classification characteristics (CCs) from the original high-dimensional chemical pattern information. Then, the SOM maps the first several CCs, which include the most useful information for pattern classification, onto a 2D plane, on which the pattern classification feature is concisely represented. To improve the learning efficiency of SOM networks, two new algorithms for dynamically adjusting the learning rate and the range of neighborhood around the winning unit were further worked out. Besides, a convenient method for detecting the topologic nature of SOM results was proposed. Finally, a typical example of mapping two classes natural spearmint essence was employed to verify the effectiveness of the new approach. The feature-topology-preserving (FTP) map obtained can well represent the classification of original patterns and is much better than what obtained by SOM alone.

Interpretations of mass spectra usually depend on explanations of the molecular structure by determination of the fragmentation ions structures. Therefore, identification of fragmentation ions formulas is an initial step of such investigations. It seems that multi-isotopic modelling of all ions proposed in fragmentation data should lead to the theoretical mass spectrum. Good adjustment of experimental and predicted spectra proves the validity of the fragmentation table, whose contents are recognised as fragmentation hypothesis. This means that multi-isotopic modelling is a helpful tool for verification of proposed fragmentation data. Applications of the method are presented for 1,1′,2,2′,3,3′-hexachloro-ferrocene, C₁₀H₄Cl₆Fe, and bis(dibutyldithiophosphate)-zinc(II), [(C₄H₉O)₂PS₂]₂Zn.

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of complexities, analyzed here, illustrate its potential for solving a large class of process optimization problems in chemical engineering.