Atom Counting Research Articles

Correlation of atomic structure and photoluminescence of the same quantum dot: pinpointing surface and internal defects that inhibit photoluminescence.

In a size regime where every atom counts, rational design and synthesis of optimal nanostructures demands direct interrogation of the effects of structural divergence of individuals on the ensemble-averaged property. To this end, we have explored the structure-function relationship of single quantum dots (QDs) via precise observation of the impact of atomic arrangement on QD fluorescence. Utilizing wide-field fluorescence microscopy and atomic number contrast scanning transmission electron microscopy (Z-STEM), we have achieved correlation of photoluminescence (PL) data and atomic-level structural information from individual colloidal QDs. This investigation of CdSe/CdS core/shell QDs has enabled exploration of the fine structural factors necessary to control QD PL. Additionally, we have identified specific morphological and structural anomalies, in the form of internal and surface defects, that consistently vitiate QD PL.

Computational prediction and validation of an expert's evaluation of chemical probes.

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Redefine the Kilogram in Terms of the Carbon-12 Atom and an Exact Value of the Avogadro Constant

We report on a method for redefinition of the kilogram by 12C, which ideally joins the atomic and the macroscopic mass units in a natural way. The kilogram artifact will be composed of a number of concentric shells around C-60 and that this is sometimes referred to as a “carbon onion”. A 135887620-layer carbon onion containing 50184508751575328771368200 atoms of 12C falls in the acceptable range for the redefinition of kilogram and is closest to the recent experimental results. An Avogadro constant is thus derived to be 602214105018903945256418.4. A perfect carbon onion is ideal for kilogram redefinition and determination of the Avogadro constant because it is expected to exhibit characteristics of central symmetry, high stability, high strength, high sphericity, low roughness, weak inter-planar force and strong in-plane bonding, easy atomic counting, and these are important for technical feasibility in further experiments.

Maximum likelihood versus likelihood-free quantum system identification in the atom maser

We consider the problem of estimating a dynamical parameter of a Markovian quantum open system (the atom maser), by performing continuous time measurements in the systemʼs output (outgoing atoms). Two estimation methods are investigated and compared. Firstly, the maximum likelihood estimator (MLE) takes into account the full measurement data and is asymptotically optimal in terms of its mean square error. Secondly, the ‘likelihood-free’ method of approximate Bayesian computation (ABC) produces an approximation of the posterior distribution for a given set of summary statistics, by sampling trajectories at different parameter values and comparing them with the measurement data via chosen statistics. Building on previous results which showed that atom counts are poor statistics for certain values of the Rabi angle, we apply MLE to the full measurement data and estimate its Fisher information. We then select several correlation statistics such as waiting times, distribution of successive identical detections, and use them as input of the ABC algorithm. The resulting posterior distribution follows closely the data likelihood, showing that the selected statistics capture ‘most’ statistical information about the Rabi angle.

Beware of machine learning-based scoring functions-on the danger of developing black boxes.

Training machine learning algorithms with protein-ligand descriptors has recently gained considerable attention to predict binding constants from atomic coordinates. Starting from a series of recent reports stating the advantages of this approach over empirical scoring functions, we could indeed reproduce the claimed superiority of Random Forest and Support Vector Machine-based scoring functions to predict experimental binding constants from protein-ligand X-ray structures of the PDBBind dataset. Strikingly, these scoring functions, trained on simple protein-ligand element-element distance counts, were almost unable to enrich virtual screening hit lists in true actives upon docking experiments of 10 reference DUD-E datasets; this is a a feature that, however, has been verified for an a priori less-accurate empirical scoring function (Surflex-Dock). By systematically varying ligand poses from true X-ray coordinates, we show that the Surflex-Dock scoring function is logically sensitive to the quality of docking poses. Conversely, our machine-learning based scoring functions are totally insensitive to docking poses (up to 10 Å root-mean square deviations) and just describe atomic element counts. This report does not disqualify using machine learning algorithms to design scoring functions. Protein-ligand element-element distance counts should however be used with extreme caution and only applied in a meaningful way. To avoid developing novel but meaningless scoring functions, we propose that two additional benchmarking tests must be systematically done when developing novel scoring functions: (i) sensitivity to docking pose accuracy, and (ii) ability to enrich hit lists in true actives upon structure-based (docking, receptor-ligand pharmacophore) virtual screening of reference datasets.

Cage-Like Nanoclusters of ZnO Probed by Time-Resolved Photoelectron Spectroscopy and Theory.

Zinc oxide nanoclusters have been predicted as promising building blocks for cluster-assembled materials with unprecedented properties. Here, for the first time these clusters are probed by time-resolved photoelectron spectroscopy and characterized in detail by density functional theory. Their validity as building blocks for cluster-assembled materials is confirmed via rigid cage-like structures facilitating three-dimensional aggregation in combination with large band gaps that are nevertheless significantly lower than any known ZnO polymorph. In addition, electron-hole pair localization in the excited state of the cluster anions combined with their structural rigidity leads to extraordinary long-lived states above the band gap virtually independent of the cluster size, defying the rule "every atom counts".

QSAR classification model for diverse series of antimicrobial agents using classification tree configured by modified particle swarm optimization

In the present study, classification tree induced by modified particle swarm optimization (MPSOCT) was invoked to construct a quantitative structure–activity relationship (QSAR) classification model for diverse series of antimicrobial agents against Candida albicans (CA). This may enable the screening of the antimicrobial agents in silico. In MPSOCT, modified particle swarm optimization (MPSO) was used to induce a globally optimal CT via simultaneously searching the optimal splitting parameters (i.e., splitting variables and values) and appropriate structure in tree. Based on more than hundred descriptors calculated by Material Studio 4.0 software, the classification model by MPSOCT categorized the compounds into the actives or inactives, compared with those by classification tree (CT), radial basis function network (RBFN), and partial least-squares discriminant analysis (PLS-DA). Experimental results revealed that MPSOCT offered more satisfactory classification accuracy than the other three classification algorithms. In addition, descriptors HOMO eigenvalue, Dipole_Y, S_ssCH2, S_dsCH, S_dO, AlogP98, Molecular flexibility, Wiener index, Kappa-2, Subgraph counts (2): path, Subgraph counts (3): cluster, Chi (0), Bond information content, Principal moment of inertia X, Ellipsoidal volume, Shadow_YZ, Shadow_ZX, Total molecular mass, and Atom count were found to play the most predominant roles in the antimicrobial activities against CA.

Picometre-precision analysis of scanning transmission electron microscopy images of platinum nanocatalysts

Measuring picometre-scale shifts in the positions of individual atoms in materials provides new insight into the structure of surfaces, defects and interfaces that influence a broad variety of materials' behaviour. Here we demonstrate sub-picometre precision measurements of atom positions in aberration-corrected Z-contrast scanning transmission electron microscopy images based on the non-rigid registration and averaging of an image series. Non-rigid registration achieves five to seven times better precision than previous methods. Non-rigidly registered images of a silica-supported platinum nanocatalyst show pm-scale contraction of atoms at a (111)/(111) corner towards the particle centre and expansion of a flat (111) facet. Sub-picometre precision and standardless atom counting with <1 atom uncertainty in the same scanning transmission electron microscopy image provide new insight into the three-dimensional atomic structure of catalyst nanoparticle surfaces, which contain the active sites controlling catalytic reactions.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp3-hybridized carbons (Fsp3), and fraction of molecular framework (fMF). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. fMF has only a minor influence besides its correlation with the other molecular descriptors.

Expanding the fragrance chemical space for virtual screening.

The properties of fragrance molecules in the public databases SuperScent and Flavornet were analyzed to define a “fragrance-like” (FL) property range (Heavy Atom Count ≤ 21, only C, H, O, S, (O + S) ≤ 3, Hydrogen Bond Donor ≤ 1) and the corresponding chemical space including FL molecules from PubChem (NIH repository of molecules), ChEMBL (bioactive molecules), ZINC (drug-like molecules), and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). The FL subsets of these databases were classified by MQN (Molecular Quantum Numbers, a set of 42 integer value descriptors of molecular structure) and formatted for fast MQN-similarity searching and interactive exploration of color-coded principal component maps in form of the FL-mapplet and FL-browser applications freely available at http://www.gdb.unibe.ch. MQN-similarity is shown to efficiently recover 15 different fragrance molecule families from the different FL subsets, demonstrating the relevance of the MQN-based tool to explore the fragrance chemical space.

Novel method to study neutron capture of 235U and 238U simultaneously at keV energies.

The neutron capture cross sections of the main uranium isotopes, (235)U and (238)U, were measured simultaneously for keV energies, for the first time by combining activation technique and atom counting of the reaction products using accelerator mass spectrometry. New data, with a precision of 3%-5%, were obtained from mg-sized natural uranium samples for neutron energies with an equivalent Maxwell-Boltzmann distribution of kT ∼ 25 keV and for a broad energy distribution peaking at 426 keV. The cross-section ratio of (235)U(n,γ)/(238)U(n,γ) can be deduced in accelerator mass spectrometry directly from the atom ratio of the reaction products (236)U/(239)U, independent of any fluence normalization. Our results confirm the values at the lower band of existing data. They serve as important anchor points to resolve present discrepancies in nuclear data libraries as well as for the normalization of cross-section data used in the nuclear astrophysics community for s-process studies.

Continuous Levels‐of‐Detail and Visual Abstraction for Seamless Molecular Visualization

AbstractMolecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level‐of‐detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent‐excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near‐, mid‐ and far‐field. Moreover, we introduce a methodology to render the entire molecule directly using the A‐buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.

Cavity-aided nondemolition measurements for atom counting and spin squeezing

Probing the collective spin state of an ensemble of atoms may provide a means to reduce heating via the photon recoil associated with the measurement and provide a robust, scalable route for preparing highly entangled states with spectroscopic sensitivity below the standard quantum limit for coherent spin states. The collective probing relies on obtaining a very large optical depth that can be effectively increased by placing the ensemble within an optical cavity such that the probe light passes many times through the ensemble. Here we provide expressions for measurement resolution and spectroscopic enhancement in such cavity-aided non-demolition measurements as a function of cavity detuning. In particular, fundamental limits on spectroscopic enhancements in $^{87}$Rb are considered.

Violation of Cauchy-Schwarz inequalities by spontaneous Hawking radiation in resonant boson structures

The violation of a classical Cauchy-Schwarz (CS) inequality is identified as an unequivocal signature of spontaneous Hawking radiation in sonic black holes. This violation can be particularly large near the peaks in the radiation spectrum emitted from a resonant boson structure forming a sonic horizon. As a function of the frequency-dependent Hawking radiation intensity, we analyze the degree of CS violation and the maximum violation temperature for a double barrier structure separating two regions of subsonic and supersonic condensate flow. We also consider the case where the resonant sonic horizon is produced by a space-dependent contact interaction. In some cases, CS violation can be observed by direct atom counting in a time-of-flight experiment. We show that near the conventional zero-frequency radiation peak, the decisive CS violation cannot occur.

Capturing protein folding-relevant topology via absolute contact order variants

Absolute contact order is one of the simplest parameters used to predict protein folding rates. Many variants of contact order (CO) have been applied to highlight different aspects of contact neighborhoods and their relationship to folding. However, a systematic study of the influence of CO variants on correlation with folding rate has not been performed for a large combined set of multi- and two-state proteins. We explore different contact neighborhoods and resulting CO by varying the distance thresholds and weighting of sequence separation for heavy atom and residue-based counting methods for a set of 136 proteins diverse across folding and structural classes. We examine the changes in contact neighborhoods and compare correlations with our CO variants and the protein folding rates across our data set as well as by folding type and structural class. Different CO variants lead to the strongest correlations within each protein structural class. Our results demonstrate that backbone topology at a distance beyond where energetic interactions dominate is able to capture folding determinants, and suggest that more sensitive methods of characterizing contact relationships may improve ln kf prediction for diverse protein sets.

Is there a Stobbs factor in atomic-resolution STEM-EELS mapping?

Recent work has convincingly argued that the Stobbs factor-disagreement in contrast between simulated and experimental atomic-resolution images-in ADF-STEM imaging can be accounted for by including the incoherent source size in simulation. However, less progress has been made for atomic-resolution STEM-EELS mapping. Here we have performed carefully calibrated EELS mapping experiments of a [101] DyScO3 single-crystal specimen, allowing atomic-resolution EELS signals to be extracted on an absolute scale for a large range of thicknesses. By simultaneously recording the elastic signal, also on an absolute scale, and using it to characterize the source size, sample thickness and inelastic mean free path, we eliminate all free parameters in the simulation of the core-loss signals. Coupled with double channeling simulations that incorporate both core-loss inelastic scattering and dynamical elastic and thermal diffuse scattering, the present work enables a close scrutiny of the scattering physics in the inelastic channel. We found that by taking into account the effective source distribution determined from the ADF images, both the absolute signal and the contrast in atomic-resolution Dy-M5 maps can be closely reproduced by the double-channeling simulations. At lower energy losses, discrepancies are present in the Sc-L2,3 and Dy-N4,5 maps due to the energy-dependent spatial distribution of the background spectrum, core-hole effects, and omitted complexities in the final states. This work has demonstrated the possibility of using quantitative STEM-EELS for element-specific column-by-column atom counting at higher energy losses and for atomic-like final states, and has elucidated several possible improvements for future theoretical work.

ChemInform Abstract: Condensation Reactions of Guanidines with Bis‐electrophiles: Formation of Highly Nitrogenous Heterocycles.

2-Amino-1,4-dihydropyrimidines were reacted with bis-electrophiles to produce novel fused bi-pyrimidine, pyrimido-aminotriazine, and pyrimido-sulfonamide scaffolds. In addition, a quinazoline library was constructed using a guanidine Atwal-Biginelli reaction with 1-(quinazolin-2-yl)guanidines. The product heterocycles have novel constitutions with high nitrogen atom counts and represent valuable additions to screening libraries for the discovery of new modulators of biological targets.

QSAR Studies of Some Thiazolo[4,5-b]pyridines as Novel Antioxidant Agents: Enhancement of Activity by Some Molecular Structure Parameters

The antioxidant activity of novel N3 and C6 substituted 5,7-dimethyl-3H-thiazolo[4,5-b]pyridine-2-one derivatives was evaluated in vitro by the method of scavenging effect on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals. The correlation analysis between the antioxidant activity and different subsets molecular descriptors was carried out for 19 compounds. The regression models derived with QSAR-analysis display the significant influence of topological structure, atom and bond type counts, physicochemical properties, and quantum-chemical structure parameters on the free radical scavenging effect of the compounds. Визначено антиоксидантну активність синтезованих похідних 5,7-диметил-тіазоло[4,5-b]піридин-2-ону, що містять замісники різної природи у 3-му та 6-му положеннях базового гетероциклу. Антиоксидантну активність сполук досліджували in vitro, визначаючи зменшення концентрації вільного радикалу 2,2-дифеніл-1-пікрилгідразилу. Для виборки з 19 похідних було проведено кореляційний аналіз залежності їх антиоксидантної активності від різних типів молекулярних дескрипторів. Аналіз рівнянь регресії, одержаних на основі проведеного QSAR аналізу, свідчить про суттєвий вплив топологічної будови молекул, кількостей атомів та зв’язків різних типів, фізико-хімічних властивостей речовин та квантово-хімічних параметрів їх структури на величини радикалпоглинаючої активності досліджуваних сполук.

Quantitative Annular Dark Field Electron Microscopy Using Single Electron Signals

One of the difficulties in analyzing atomic resolution electron microscope images is that the sample thickness is usually unknown or has to be fitted from parameters that are not precisely known. An accurate measure of thickness, ideally on a column-by-column basis, parameter free, and with single atom accuracy, would be of great value for many applications, such as matching to simulations. Here we propose such a quantification method for annular dark field scanning transmission electron microscopy by using the single electron intensity level of the detector. This method has the advantage that we can routinely quantify annular dark field images operating at both low and high beam currents, and under high dynamic range conditions, which is useful for the quantification of ultra-thin or light-element materials. To facilitate atom counting at the atomic scale we use the mean intensity in an annular dark field image averaged over a primitive cell, with no free parameters to be fitted. To illustrate the potential of our method, we demonstrate counting the number of Al (or N) atoms in a wurtzite-type aluminum nitride single crystal at each primitive cell over the range of 3-99 atoms.

Scale Adaptive Dictionary Learning

Dictionary learning has been widely used in many image processing tasks. In most of these methods, the number of basis vectors is either set by experience or coarsely evaluated empirically. In this paper, we propose a new scale adaptive dictionary learning framework, which jointly estimates suitable scales and corresponding atoms in an adaptive fashion according to the training data, without the need of prior information. We design an atom counting function and develop a reliable numerical scheme to solve the challenging optimization problem. Extensive experiments on texture and video data sets demonstrate quantitatively and visually that our method can estimate the scale, without damaging the sparse reconstruction ability.