Generation of Total Angular Momentum Eigenstates in Remote Qubits

We propose a scheme enabling the universal coupling of angular momentum of $N$ remote noninteracting qubits using linear optical tools only. Our system consists of $N$ single-photon emitters in a $\Lambda$-configuration that are entangled among their long-lived ground-state qubits through suitably designed measurements of the emitted photons. In this manner, we present an experimentally feasible algorithm that is able to generate any of the $2^N$ symmetric and nonsymmetric total angular momentum eigenstates spanning the Hilbert space of the $N$-qubit compound.Comment: 5 pages, 4 figures, improved presentation. Accepted in Physical Review

SeO₂-Mediated Oxidative Transposition of Pauson–Khand Products

Oxidative transpositions of bicyclic cyclopentenones mediated by selenium dioxide (SeO₂) are disclosed. Treatment of Pauson–Khand reaction (PKR) products with SeO₂ in the presence or absence of water furnishes di- and trioxidized cyclopentenones, respectively. Mechanistic investigations reveal multiple competing oxidation pathways that depend on substrate identity and water concentration. Functionalization of the oxidized products via cross-coupling methods demonstrates their synthetic utility. These transformations allow rapid access to oxidatively transposed cyclopentenones from simple PKR products

Takagi-Taupin Description of X-ray Dynamical Diffraction from Diffractive Optics with Large Numerical Aperture

We present a formalism of x-ray dynamical diffraction from volume diffractive optics with large numerical aperture and high aspect ratio, in an analogy to the Takagi-Taupin equations for strained single crystals. We derive a set of basic equations for dynamical diffraction from volume diffractive optics, which enable us to study the focusing property of these optics with various grating profiles. We study volume diffractive optics that satisfy the Bragg condition to various degrees, namely flat, tilted and wedged geometries, and derive the curved geometries required for ultimate focusing. We show that the curved geometries satisfy the Bragg condition everywhere and phase requirement for point focusing, and effectively focus hard x-rays to a scale close to the wavelength.Comment: 18 pages, 12 figure

Nanoscale Charge Density and Dynamics in Graphene Oxide

Graphene oxide (GO) is widely used as a component in thin film optoelectronic device structures for practical reasons because its electronic and optical properties can be controlled. Progress critically depends on elucidating the nanoscale electronic structure of GO. However, direct experimental access is challenging because of its disordered and nonconductive character. Here, we quantitatively mapped the nanoscopic charge distribution and charge dynamics of an individual GO sheet by using Kelvin probe force microscopy (KPFM). Charge domains are identified, presenting important charge interactions below distances of 20 nm. Charge dynamics with very long relaxation times of at least several hours and a logarithmic decay of the time correlation function are in excellent agreement with Monte Carlo simulations, revealing an universal hopping transport mechanism best described by Efros-Shklovskii''s law. © 2021 The Authors. Published by American Chemical Society

3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries

We investigate 3D deep learning methods for predicting quantum mechanical energies at high-theory-level accuracy from inexpensive, rapidly computed molecular geometries. Using space-filled volumetric representations (voxels), we explore the effects of radial decay from atom centers and rotational data augmentation on learnability. We test several published computer vision models for 3D shape learning, and construct our own architecture based on 3D inception networks with physically meaningful kernels. We provide a framework for further studies and propose a modeling challenge for the computer vision and molecular machine learning communities

Evaluation of cortisol precursors for the diagnosis of pituitary-dependent hypercortisolism in dogs

The serum concentrations of cortisol, 17alpha-hydroxypregnenolone, 17alpha-hydroxyprogesterone, 21-deoxycortisol and 11-deoxycortisol were measured in 19 healthy dogs, 15 dogs with pituitary-dependent hypercortisolism (pdh) and eight dogs with other diseases before and one hour after an injection of synthetic adrenocorticotrophic hormone (acth). At both times the dogs with pdh had significantly higher concentrations of cortisol, 17alpha-hydroxypregnenolone, 17alpha-hydroxyprogesterone and 21-deoxycortisol than the healthy dogs. Basal 11-deoxycortisol concentrations were also significantly higher in dogs with pdh compared with healthy dogs. When compared with the dogs with other diseases, the dogs with pdh had significantly higher basal and post-acth cortisol and basal 21-deoxycortisol, and significantly lower post-acth 11-deoxycortisol concentrations. The dogs with other diseases had significantly higher post-acth cortisol, 17alpha-hydroxyprogesterone and 11-deoxycortisol concentrations than the healthy dogs. In general, the post-acth concentrations of 17alpha-hydroxypregnenolone, 17alpha-hydroxyprogesterone, 11-deoxycortisol and 21-deoxycortisol were more variable than the post-acth concentrations of cortisol, resulting in large overlaps of the concentrations of these hormones between the three groups. A two-graph receiver operating characteristic (ROC) analysis was used to maximise the sensitivity and specificity of each hormone for diagnosing hypercortisolism; it showed that the post-acth concentration of cortisol had the highest sensitivity and specificity. The overlaps between the healthy dogs, the dogs with pdh and the dogs with other diseases suggested that the individual precursor hormones would not be useful as a screening test for hypercortisolism

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB