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

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

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

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

Graphene oxide: key to efficient charge extraction and suppression of polaronic transport in hybrids with poly (3-hexylthiophene) nanoparticles

Nanoparticles (NPs) of conjugated polymers in intimate contact with sheets of graphene oxide (GO) constitute a promising class of water-dispersible nanohybrid materials of increased interest for the design of sustainable and improved optoelectronic thin-film devices, revealing properties exclusively pre-established upon their liquid-phase synthesis. In this context, we report for the first time the preparation of a P3HTNPs–GO nanohybrid employing a miniemulsion synthesis approach, whereby GO sheets dispersed in the aqueous phase serve as a surfactant. We show that this process uniquely favors a quinoid-like conformation of the P3HT chains of the resulting NPs well located onto individual GO sheets. The accompanied change in the electronic behavior of these P3HTNPs, consistently confirmed by the photoluminescence and Raman response of the hybrid in the liquid and solid states, respectively, as well as by the properties of the surface potential of isolated individual P3HTNPs–GO nano-objects, facilitates unprecedented charge transfer interactions between the two constituents. While the electrochemical performance of nanohybrid films is featured by fast charge transfer processes, compared to those taking place in pure P3HTNPs films, the loss of electrochromic effects in P3HTNPs–GO films additionally indicates the unusual suppression of polaronic charge transport processes typically encountered in P3HT. Thus, the established interface interactions in the P3HTNPs–GO hybrid enable a direct and highly efficient charge extraction channel via GO sheets. These findings are of relevance for the sustainable design of novel high-performance optoelectronic device structures based on water-dispersible conjugated polymer nanoparticles