Tunneling Ionization Rates from Arbitrary Potential Wells

We present a practical numerical technique for calculating tunneling ionization rates from arbitrary 1-D potential wells in the presence of a linear external potential by determining the widths of the resonances in the spectral density, rho(E), adiabatically connected to the field-free bound states. While this technique applies to more general external potentials, we focus on the ionization of electrons from atoms and molecules by DC electric fields, as this has an important and immediate impact on the understanding of the multiphoton ionization of molecules in strong laser fields.Comment: 13 pages, 7 figures, LaTe

Predicting toxicity through computers: a changing world

The computational approaches used to predict toxicity are evolving rapidly, a process hastened on by the emergence of new ways of describing chemical information. Although this trend offers many opportunities, new regulations, such as the European Community's 'Registration, Evaluation, Authorisation and Restriction of Chemicals' (REACH), demand that models be ever more robust

A kinetic and theoretical study of the borate catalysed reactions of hydrogen peroxide: the role of dioxaborirane as the catalytic intermediate for a wide range of substrates

Our recent work has provided new insights into the equilibria and species that exist in aqueous solution at different pHs for the boric acid – hydrogen peroxide system, and the role of these species in oxidation reactions. Most recently, (M. C. Durrant, D. M. Davies and M. E. Deary, Org. Biomol. Chem., 2011, 9,7249–7254), we have produced strong theoretical and experimental evidence for the existence of a previously unreported monocyclic three membered peroxide species, dioxaborirane, that is the likely catalytic species in borate mediated electrophilic reactions of hydrogen peroxide in alkaline solution. In the present paper, we extend our study of the borate–peroxide system to look at a wide range of substrates that include substituted dimethyl anilines, methyl-p-tolyl sulfoxide, halides, hydrogen sulfide anion, thiosulfate ,thiocyanate, and hydrazine. The unusual selectivity–reactivity pattern of borate catalysed reactions compared with hydrogen peroxide and inorganic or organic peracids previously observed for theorganic sulfides (D. M. Davies, M. E. Deary, K. Quill and R. A. Smith, Chem.–Eur. J., 2005, 11, 3552–3558) is also seen with substituted dimethyl aniline nucleophiles. This provides evidence that the pattern is not due to any latent electrophilic tendency of the organic sulfides and further supports dioxaborirane being the likely reactive intermediate, thus broadening the applicability of this catalytic system. Moreover, density functional theory calculations on our proposed mechanism involving dioxaborirane are consistent with the experimental results for these substrates. Results obtained at high concentrations of both borate and hydrogen peroxide require the inclusion the diperoxodiborate dianion in the kinetic analysis .A scheme detailing our current understanding of the borate–peroxide system is presented

Time-dependent calculation of ionization in Potassium at mid-infrared wavelengths

We study the dynamics of the Potassium atom in the mid-infrared, high intensity, short laser pulse regime. We ascertain numerical convergence by comparing the results obtained by the direct expansion of the time-dependent Schroedinger equation onto B-Splines, to those obtained by the eigenbasis expansion method. We present ionization curves in the 12-, 13-, and 14-photon ionization range for Potassium. The ionization curve of a scaled system, namely Hydrogen starting from the 2s, is compared to the 12-photon results. In the 13-photon regime, a dynamic resonance is found and analyzed in some detail. The results for all wavelengths and intensities, including Hydrogen, display a clear plateau in the peak-heights of the low energy part of the Above Threshold Ionization (ATI) spectrum, which scales with the ponderomotive energy Up, and extends to 2.8 +- 0.5 Up.Comment: 15 two-column pages with 15 figures, 3 tables. Accepted for publication in Phys. Rev A. Improved figures, language and punctuation, and made minor corrections. We also added a comparison to the ADK theor

Resonant enhancements of high-order harmonic generation

Solving the one-dimensional time-dependent Schr\"odinger equation for simple model potentials, we investigate resonance-enhanced high-order harmonic generation, with emphasis on the physical mechanism of the enhancement. By truncating a long-range potential, we investigate the significance of the long-range tail, the Rydberg series, and the existence of highly excited states for the enhancements in question. We conclude that the channel closings typical of a short-range or zero-range potential are capable of generating essentially the same effects.Comment: 7 pages revtex, 4 figures (ps files

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed

Beyond Implications and Applications: the Story of ‘Safety by Design’

Using long-term anthropological observations at the Center for Biological and Environmental Nanotechnology in Houston, Texas, the article demonstrates in detail the creation of new objects, new venues and new modes of veridiction which have reoriented the disciplines of materials chemistry and nanotoxicology. Beginning with the confusion surrounding the meaning of ‘implications’ and ‘applications’ the article explores the creation of new venues (CBEN and its offshoot the International Council on Nanotechnology); it then demonstrates how the demands for a responsible, safe or ethical science were translated into new research and experiment in and through these venues. Finally it shows how ‘safety by design’ emerged as a way to go beyond implications and applications, even as it introduced a whole new array of controversies concerning its viability, validity and legitimacy

A Strategy for Structuring and Reporting a Read-Across Prediction of Toxicity

Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the 50 context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p