jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xian‐Sheng Ke | Yongseok Hong | Vincent M. Lynch | Dongho Kim | Jonathan L. Sessler
Source: Journal of the American Chemical Society (article, 2018-05-22)
Field: Materials Science — Materials Chemistry

Metal-Stabilized Quinoidal Dibenzo[<i>g</i>, <i>p</i>]chrysene-Fused Bis-dicarbacorrole System We report here a metal complexation-based strategy that permits access to a highly stable expanded porphyrin-type quinoidal polycyclic aromatic hydrocarbons (PAH). Specifically, double insertion of Pd(II) ions into a dibenzo[ g, p]chrysene-fused bis-dicarbacorrole (bis-H<sub>3</sub>) gives rise to a bis-metalated species (bis-Pd) that undergoes a facile benzenoid-quinonoid transformation. In contrast to what is true for the corresponding mono-Pd(II) complex, which has organic radical character, well resolved <sup>1</sup>H NMR and <sup>19</sup>F NMR spectra are seen for bis-Pd. This complex is also electron paramagnetic resonance (EPR) silent over a range of temperatures. On the basis of crystallographic analyses, Raman spectroscopic studies, harmonic oscillator model of aromaticity (HOMA), and nucleus-independent chemical shift (NICS) calculations, we suggest that the dibenzo[ g, p]chrysene bridge in bis-Pd has quinoidal character and that the system as a whole is a closed shell species. As expected for a quinoidal system, bis-Pd is characterized by a lowest energy absorption band that is shifted into the NIR (λ<sub>max</sub> = ca. 1420 nm (ε > 1.5 × 10<sup>5</sup> M<sup>-1</sup> cm<sup>-1</sup>) for bis-Pd vs 780 nm (ε < 5.0 × 10<sup>3</sup> M<sup>-1</sup> cm<sup>-1</sup>) for bis-H<sub>3</sub>). On the other hand, bis-Pd displays solvent dependent ground state and transient absorption spectral features. Such findings provide support for a zwitterionic resonance contribution to what is a predominantly a quinonoid-type ground state. The use of specific metalation to fine-tune the electronic features of polytopic ligands, as reported here, opens the door to what might be a potentially generalizable approach to the design of quinoidal PAH structures with long wavelength solvatochromic absorption features. Chrysene ; Chemistry ; Metal ; Stereochemistry ; Medicinal chemistry ; Organic chemistry ; Pyrene Porphyrin and Phthalocyanine Chemistry ; Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry
Authors: Theresa Fischer | Julia Bamberger | Melania Gómez‐Martínez | Dariusz G. Piekarski | Olga Garcı́a Mancheño
Source: Angewandte Chemie International Edition (article, 2018-11-14)
Field: Chemistry — Inorganic Chemistry

Helical Multi‐Coordination Anion‐Binding Catalysts for the Highly Enantioselective Dearomatization of Pyrylium Derivatives A general and highly enantioselective synthesis of oxygen heterocycles from readily available in situ generated pyrylium derivatives has been realized by embracing a multi-coordination approach with helical anion-binding tetrakistriazole catalysts. The high activity of the tetrakistriazole (TetraTri) catalysts, with distinct confined anion-binding pockets, allows for remarkably low catalyst loadings (down to 0.05 mol %), while providing a simple access to chiral chromanones and dihydropyrones in high enantioselectivities (up to 98:2 e.r.). Moreover, experimental and theoretical studies provide new insights into the hydrogen-donor ability and key binding interactions of the TetraTri catalysts and its host:guest complexes, suggesting the formation of a 1:3 species. Enantioselective synthesis ; Catalysis ; Chemistry ; Combinatorial chemistry ; Ion ; Stereochemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Axial and Atropisomeric Chirality Synthesis ; Asymmetric Synthesis and Catalysis
Authors: Morwen Williams | Benoı̂t Bertrand | David L. Hughes | Zoë A. E. Waller | Claudia Schmidt | Ingo Ott | Maria A. O’Connell | Mark Searcey | Manfred Bochmann
Source: Metallomics (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Cyclometallated Au(<scp>iii</scp>) dithiocarbamate complexes: synthesis, anticancer evaluation and mechanistic studies A series of cationic mixed cyclometallated (C^N)Au(iii) dithiocarbamate complexes has been synthesized in good yields [HC^N = 2-(p-t-butylphenyl)pyridine]. The crystal structure of [(C^N)AuS2CNEt2]PF6 (3) has been determined. The cytotoxic properties of the new complexes have been evaluated in vitro against a panel of human cancer cell lines and healthy cells and compared with a neutral mixed (C^C)Au(iii) dithiocarbamate complex (C^C = 4,4'-di-t-butylbiphenyl-2,2'-diyl). The complexes appeared to be susceptible to reduction by glutathione but were stable in the presence of N-acetyl cysteine. The potential mechanism of action of this class of compounds has been investigated by measuring the intracellular uptake of some selected complexes, by determining their interactions with higher order DNA structures, and by assessing the ability to inhibit thioredoxin reductase. The complexes proved unable to induce the formation of reactive oxygen species. The investigations add to the picture of the possible mode of action of this class of complexes. Dithiocarbamate ; Chemistry ; Combinatorial chemistry ; Organic chemistry Organometallic Compounds Synthesis and Characterization ; Metal complexes synthesis and properties ; Ferrocene Chemistry and Applications
Authors: Kan Hatakeyama‐Sato | Toshiki Tezuka | Yoshinori Nishikitani | Hiroyuki Nishide | Kenichi Oyaizu
Source: Chemistry Letters (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening A database for 240 types of lithium-ion conducting solid polymer electrolytes was newly constructed and analyzed by machine learning. Despite the complexity of the polymer composites as electrolyte... Chemistry ; Lithium (medication) ; Ion ; Polymer ; Nanotechnology ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Soumya Sanyal | Janakiraman Balachandran | Naganand Yadati | Abhishek Kumar | Padmini Rajagopalan | Suchismita Sanyal | Partha Talukdar
Source: arXiv (Cornell University) (preprint, 2018-11-14)
Field: Materials Science — Materials Chemistry

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction Developing accurate, transferable and computationally inexpensive machine learning models can rapidly accelerate the discovery and development of new materials. Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties. The limited availability of materials data can be addressed through transfer learning, while the generic representation was recently addressed by Xie and Grossman [1], where they developed a crystal graph convolutional neural network (CGCNN) that provides a unified representation of crystals. In this work, we develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN by simultaneous prediction of various material properties such as Formation Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774 materials). MT-CGCNN is able to reduce the test error when employed on correlated properties by upto 8%. The model prediction has lower test error compared to CGCNN, even when the training data is reduced by 10%. We also demonstrate our model's better performance through prediction of end user scenario related to metal/non-metal classification. These results encourage further development of machine learning approaches which leverage multi-task learning to address the aforementioned challenges in the discovery of new materials. We make MT-CGCNN's source code available to encourage reproducible research. Convolutional neural network ; Graph ; Computer science ; Property (philosophy) ; Artificial intelligence ; Multi-task learning ; Machine learning ; Theoretical computer science ; Engineering ; Task (project management) ; Systems engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tetsuro Ueno | Hideitsu Hino | Ai Hashimoto | Yasuo Takeichi | Masahiro Sawada | Kanta Ono
Source: npj Computational Materials (article, 2018-01-15)
Field: Materials Science — Materials Chemistry

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling Abstract Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies. Spectroscopy ; Magnetic circular dichroism ; X-ray magnetic circular dichroism ; Gaussian ; Process (computing) ; Gaussian process ; Computational physics ; Materials science ; Biological system ; Nuclear magnetic resonance ; Optics ; Computer science ; Physics ; Chemistry ; Spectral line ; Computational chemistry Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Gene expression and cancer classification
Authors: Yafu Guan | Dong H. Zhang | Hua Guo | David R. Yarkony
Source: Physical Chemistry Chemical Physics (article, 2018-11-17)
Field: Materials Science — Materials Chemistry

Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 <sup>2</sup>A′ states of LiFH An analytic quasi-diabatic representation of ab initio electronic structure data is key to the accurate quantum mechanical description of non-adiabatic chemical processes. In this work, a general neural network (NN) fitting procedure is proposed to generate coupled quasi-diabatic Hamiltonians (H<sup>d</sup>) that are capable of representing adiabatic energies, energy gradients, and derivative couplings over a wide range of geometries. The quasi-diabatic representation for LiFH is used as a testing example. The fitting data including adiabatic energies, energy gradients and interstate couplings are obtained from a previously fitted analytical quasi-diabatic potential energy matrix, and are well reproduced by the NN fitting. Most importantly, the NN fitting also yields quantum dynamic results that reproduce those on the original LiFH diabatic Hamiltonian, demonstrating the ability of NN to generate highly accurate quasi-diabatic Hamiltonians. Diabatic ; Adiabatic process ; Representation (politics) ; Potential energy ; Artificial neural network ; Physics ; Energy (signal processing) ; Statistical physics ; Atomic physics ; Quantum mechanics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Rebecca Lindsey | Laurence E. Fried | Nir Goldman
Source: Journal of Chemical Theory and Computation (article, 2018-11-26)
Field: Chemistry — Spectroscopy

Application of the ChIMES Force Field to Nonreactive Molecular Systems: Water at Ambient Conditions We demonstrate development of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) for molecular systems through application to water under ambient conditions (298 K, 1 g/cm<sup>3</sup>). These models, which are comprised of linear combinations of Chebyshev polynomials explicitly describing two- and three-body interactions, are largely fit by force matching to Kohn-Sham Density Functional Theory (DFT). Protocols for selecting user-specified parameters and inclusion of stress tensor data are investigated, and structural and dynamical property prediction for resulting models is benchmarked against DFT. We show that the present ChIMES force fields yield excellent agreement with DFT without the need for additional terms such as those for Coulomb interactions. Overall, we show that tractable parametrization and subsequent accuracy of the present models make ChIMES an ideal candidate for extension of DFT dynamics to larger system sizes and longer time scales. Parametrization (atmospheric modeling) ; Chebyshev polynomials ; Force field (fiction) ; Coulomb ; Density functional theory ; Correctness ; Chebyshev filter ; Matching (statistics) ; Field (mathematics) ; Molecular dynamics ; Tensor (intrinsic definition) ; Statistical physics ; Computer science ; Physics ; Mathematics ; Algorithm ; Mathematical analysis ; Geometry ; Quantum mechanics ; Pure mathematics Advanced NMR Techniques and Applications ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: P. Neugebauer | Javier Cardona | Maximilian O. Besenhard | Anna Peter | Heidrun Gruber‐Wölfler | Christos Tachtatzis | Alison Cleary | Ivan Andonović | Ján Šefčı́k | Johannes Khinast
Source: Crystal Growth & Design (article, 2018-06-15)
Field: Materials Science — Materials Chemistry

Crystal Shape Modification via Cycles of Growth and Dissolution in a Tubular Crystallizer Besides size and polymorphic form, crystal shape takes a central role in engineering advanced solid materials for the pharmaceutical and chemical industries. This work demonstrates how multiple cycles of growth and dissolution can manipulate the habit of an acetylsalicylic acid crystal population. Considerable changes of the crystal habit could be achieved within minutes due to rapid cycling, i.e., up to 25 cycles within <10 min. The required fast heating and cooling rates were facilitated using a tubular reactor design allowing for superior temperature control. The face-specific interactions between solvent and the crystals' surface result in face-specific growth and dissolution rates and hence alterations of the final shape of the crystals in solution. Accurate quantification of the crystal shapes was essential for this work, but is everything except simple. A commercial size and shape analyzer had to be adapted to achieve the required accuracy. Online size, and most important shape, analysis was achieved using an automated microscope equipped with a flow-through cell, in combination with a dedicated image analysis routine for particle tracking and shape analysis. Due to the implementation of this analyzer, capable of obtaining statistics on the crystals' shape while still in solution (no sampling and manipulation required), the dynamic behavior of the size shape distribution could be studied. This enabled a detailed analysis of the solvent's effect on the change in crystal habit. Dissolution ; Crystal habit ; Crystal (programming language) ; Particle size ; Crystal growth ; Materials science ; Spectrum analyzer ; Chemistry ; Particle (ecology) ; Biological system ; Crystallography ; Nanotechnology ; Crystallization ; Computer science ; Optics ; Organic chemistry ; Physics ; Physical chemistry Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Calcium Carbonate Crystallization and Inhibition
Authors: Corey Oses | Cormac Toher | Stefano Curtarolo
Source: MRS Bulletin (article, 2018-09-01)
Field: Materials Science — Materials Chemistry

Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery Nanotechnology ; Materials science ; Computer science ; Systems engineering ; Data science ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Masanobu Nakayama | Kenta Kanamori | K. Nakano | Randy Jalem | Ichiro Takeuchi | Hisatsugu Yamasaki
Source: The Chemical Record (article, 2018-11-30)
Field: Materials Science — Materials Chemistry

Data‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations Abstract Interest in all‐solid‐state Li‐ion batteries (LIBs) using non‐flammable Li‐conducting ceramics as solid electrolytes has increased, as safe and robust batteries are urgently desired as power sources for (hybrid) electric vehicles. However, the low Li‐ion conductivities of ceramics have hindered all‐solid‐state LIB commercialization; many researchers have attempted to develop fast Li‐ion conductors. We introduce two efficient high‐throughput computational approaches for materials exploration: (i) exhaustive search and (ii) informatics‐aided prediction. For demonstration, ∼400 Li‐ and Zn‐containing oxide (Li−Zn−X−O) compounds of varied crystal structures are extracted from Materials Project datasets. We calculate the migration energies for Li‐ion conduction and the phase stabilities (decomposition energies) of these materials by simulation and apply Bayesian optimization to determine the material with the highest ionic conductivity. The results show much greater efficiency than a random search algorithm. Fast ion conductor ; Ceramic ; Materials science ; Ion ; Conductivity ; Electrical conductor ; Materials informatics ; Commercialization ; Ionic conductivity ; Flammable liquid ; Oxide ; Engineering physics ; Computer science ; Electrolyte ; Process engineering ; Nanotechnology ; Thermodynamics ; Composite material ; Chemistry ; Physical chemistry ; Physics ; Engineering ; Electrode Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Jeffrey D. Rimer | Aseem Chawla | Thuy T. Le
Source: Annual Review of Chemical and Biomolecular Engineering (review, 2018-03-23)
Field: Chemistry — Inorganic Chemistry

Crystal Engineering for Catalysis Crystal engineering relies upon the ability to predictively control intermolecular interactions during the assembly of crystalline materials in a manner that leads to a desired (and predetermined) set of properties. Economics, scalability, and ease of design must be leveraged with techniques that manipulate the thermodynamics and kinetics of crystal nucleation and growth. It is often challenging to exact simultaneous control over multiple physicochemical properties, such as crystal size, habit, chirality, polymorph, and composition. Engineered materials often rely upon postsynthesis (top-down) processes to introduce properties that would otherwise be challenging to attain through direct (bottom-up) approaches. We discuss the application of crystal engineering to heterogeneous catalysts with a focus on four general themes: ( a) tailored nanocrystal size, ( b) controlled environments surrounding active sites, ( c) tuned morphology with well-defined facets, and ( d) hierarchical materials with disparate pore size and active site distributions. We focus on nonporous materials, including metals and metal oxides, and two classes of porous materials: zeolites and metal organic frameworks. We review novel synthesis methods involving synergistic experimental and computational design approaches, the challenges facing catalyst development, and opportunities for future advancement in crystal engineering. Crystal engineering ; Nanotechnology ; Nucleation ; Crystal (programming language) ; Materials science ; Nanocrystal ; Catalysis ; Intermolecular force ; Crystal growth ; Crystal habit ; Chemistry ; Crystallization ; Crystal structure ; Computer science ; Crystallography ; Supramolecular chemistry ; Molecule ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications
Authors: Vito Dozio | Jean‐Charles Sanchez
Source: Journal of Neuroinflammation (article, 2018-11-30)
Field: Chemistry — Spectroscopy

Profiling the proteomic inflammatory state of human astrocytes using DIA mass spectrometry The present study provides comprehensive information about the proteomic phenotypes of human astrocytes upon exposure to inflammatory stimuli both in monoculture and in co-culture with human brain microvascular endothelial cells. Mass spectrometry ; Proteomics ; Profiling (computer programming) ; Neurology ; Computational biology ; Chemistry ; Medicine ; Neuroscience ; Biology ; Chromatography ; Computer science ; Biochemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Immune Response and Inflammation
Authors: Hiromu Hattori | Elias Kaufmann | Hideki Miyatake‐Ondozabal | Regina Berg | Karl Gademann
Source: The Journal of Organic Chemistry (article, 2018-03-29)
Field: Chemistry — Organic Chemistry

Total Synthesis of Tiacumicin A. Total Synthesis, Relay Synthesis, and Degradation Studies of Fidaxomicin (Tiacumicin B, Lipiarmycin A3) The commercial macrolide antibiotic fidaxomicin was synthesized in a highly convergent manner. Salient features of this synthesis include a β-selective noviosylation, a β-selective rhamnosylation, a ring-closing metathesis, a Suzuki coupling, and a vinylogous Mukaiyama aldol reaction. Careful choice of protecting groups and fine-tuning of the glycosylation reactions led to the first total synthesis of fidaxomicin. In addition, a relay synthesis of fidaxomicin was established, which gives access to a conveniently protected intermediate from the natural material for derivatization. The first total synthesis of a related congener, tiacumicin A, is presented. Total synthesis ; Fidaxomicin ; Aldol reaction ; Chemistry ; Glycosylation ; Derivatization ; Stereochemistry ; Organic chemistry ; Biology ; Catalysis ; Biochemistry Synthetic Organic Chemistry Methods ; Carbohydrate Chemistry and Synthesis ; Asymmetric Synthesis and Catalysis
Authors: S.M. Savić | Katarina Vojisavljević | Milica Počuča-Nešić | Kristina Živojević | Minja Mladenović | Nikola Ž. Knežević
Source: Metallurgical and Materials Engineering (article, 2018-12-20)
Field: Materials Science — Materials Chemistry

Hard Template Synthesis of Nanomaterials Based on Mesoporous Silica Diverse hard template synthetic methodologies are being employed for the synthesis of mesostructured metal oxide and carbon nanomaterials, with the application of mesoporous silica as the hard template. We describe the main differences and advantages/disadvantages between the soft and hard templated synthetic routes, provide an overview of the synthesis and characteristics of different templating mesoporous silica nanomaterials and discuss on practical aspects of the hard template synthetic methodology for obtaining various metal-oxide and carbon-based mesostructured nanomaterials. Also, we cover various recent applications of thus constructed mesostructured metal oxide and carbon nanomaterials, such as sensing, energy storage, fuel cells, and catalysis, which demonstrate the highly promising character of the hard template methodology for the synthesis of a new generation of nanomaterials with broad application potential. Nanomaterials ; Materials science ; Nanotechnology ; Mesoporous silica ; Mesoporous material ; Template method pattern ; Carbon fibers ; Oxide ; Catalysis ; Composite number ; Chemistry ; Organic chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions