jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kari Nurmi | Heli Jantunen | Jari Juuti
Source: Journal of the European Ceramic Society (article, 2018-12-19)
Field: Materials Science — Materials Chemistry

The effect of titanium excess and deficiency on the microstructure and dielectric properties of lanthanum doped Ba0.55Sr0.45TiO3 with colossal permittivity Materials science ; Permittivity ; Lanthanum ; Dielectric ; Microstructure ; Doping ; Analytical Chemistry (journal) ; Titanium ; Dielectric permittivity ; Lanthanum oxide ; Perovskite (structure) ; Atmospheric temperature range ; Electroceramics ; Grain size ; Grain growth ; Mineralogy ; Composite material ; Oxide ; Metallurgy ; Chemical engineering ; Inorganic chemistry ; Optoelectronics ; Thermodynamics Dielectric properties of ceramics ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Ke Liu | Jonas Greitemann | Lode Pollet
Source: Physical review. E (article, 2018-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generic first-order phase transitions between isotropic and orientational phases with polyhedral symmetries Polyhedral nematics are examples of exotic orientational phases that possess a complex internal symmetry, representing highly nontrivial ways of rotational symmetry breaking, and are subject to current experimental pursuits in colloidal and molecular systems. The classification of these phases has been known for a long time; however, their transitions to the disordered isotropic liquid phase remain largely unexplored, except for a few symmetries. In this work, we utilize a recently introduced non-Abelian gauge theory to explore the nature of the underlying nematic-isotropic transition for all three-dimensional polyhedral nematics. The gauge theory can readily be applied to nematic phases with an arbitrary point-group symmetry, including those where traditional Landau methods and the associated lattice models may become too involved to implement owing to a prohibitive order-parameter tensor of high rank or (the absence of) mirror symmetries. By means of exhaustive Monte Carlo simulations, we find that the nematic-isotropic transition is generically first-order for all polyhedral symmetries. Moreover, we show that this universal result is fully consistent with our expectation from a renormalization group approach, as well as with other lattice models for symmetries already studied in the literature. We argue that extreme fine tuning is required to promote those transitions to second-order ones. We also comment on the nature of phase transitions breaking the O(3) symmetry in general cases. Homogeneous space ; Isotropy ; Phase transition ; Order (exchange) ; Phase (matter) ; Theoretical physics ; Materials science ; Condensed matter physics ; Physics ; Mathematics ; Geometry ; Quantum mechanics Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Chang-Chih Hsieh | Yu-Chiao Liu | Mei‐Chun Tseng | Ming‐Hsi Chiang | Yih‐Chern Horng
Source: Dalton Transactions (article, 2018-11-27)
Field: Chemistry — Inorganic Chemistry

Dioxygen activation by a dinuclear thiolate-ligated Fe(<scp>ii</scp>) complex Dioxygen activation by FeII thiolate complexes is relatively rare in biological and chemical systems because the sulfur site is at least as vulnerable as the iron site to oxidative modification. O2 activation by FeII-SR complexes with thiolate bound trans to the O2 binding site generally affords the FeIV[double bond, length as m-dash]O intermediate and oxidized thiolate. On the other hand, O2 activation by Fe(ii)-SR complexes with thiolate bound cis to the O2 binding site generates FeIII-O-FeIII or S-oxygenated complexes. The postulated FeIV[double bond, length as m-dash]O intermediate has only been identified in isopenicillin N synthase recently. We demonstrated here that O2 activation by a dinuclear FeII thiolate-rich complex produces a mononuclear FeIII complex and water with a supply of electron donors. The thiolate is bound cis to the postulated dioxygen binding site, and no FeIII-O-FeIII or S-oxygenated complex was observed. Although we have not detected the transient intermediate by spectroscopic measurements, the FeIV[double bond, length as m-dash]O intermediate is suggested to exist by theoretical calculation, and P-oxidation and hydride-transfer experiments. In addition, an unprecedented FeIII-O2-FeIII complex supported by thiolates was observed during the reaction by using a coldspray ionization time-of-flight mass (CSI-TOF MS) instrument. This is also supported by low-temperature UV-vis measurements. The intramolecular NHO[double bond, length as m-dash]FeIV hydrogen bonding, calculated by DFT, probably fine tunes the O2-activation process for intramolecular hydrogen abstraction, avoiding the S-oxygenation at cis-thiolate. Chemistry ; Stereochemistry ; Crystallography ; Medicinal chemistry Metal-Catalyzed Oxygenation Mechanisms ; Metal complexes synthesis and properties ; Porphyrin and Phthalocyanine Chemistry
Authors: A. S. Athira | S. Anu | S. R. Seeja | M. S. Thaifa
Source: International Journal for Pharmaceutical Research Scholars (review, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Review on Pharmaceutical Cocrystals Cocrystals are multicomponent systems in which two components, an active pharmaceutical ingredient, and a coformer were present in a stoichiometric ratio and bonded together with non-covalent interactions in the crystal lattice. Pharmaceutical sciences ; Traditional medicine ; Materials science ; Medicine ; Pharmacology Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Yannick Ney | Muhammad Jawad Nasim | Ammar Kharma | Lama A. Youssef | Claus Jacob
Source: Molecules (review, 2018-03-27)
Field: Chemistry — Organic Chemistry

Small Molecule Catalysts with Therapeutic Potential Catalysts are employed in many areas of research and development where they combine high efficiency with often astonishing selectivity for their respective substrates. In biology, biocatalysts are omnipresent. Enzymes facilitate highly controlled, sophisticated cellular processes, such as metabolic conversions, sensing and signalling, and are prominent targets in drug development. In contrast, the therapeutic use of catalysts per se is still rather limited. Recent research has shown that small molecule catalytic agents able to modulate the redox state of the target cell bear considerable promise, particularly in the context of inflammatory and infectious diseases, stroke, ageing and even cancer. Rather than being "active" on their own in a more traditional sense, such agents develop their activity by initiating, promoting, enhancing or redirecting reactions between biomolecules already present in the cell, and their activity therefore depends critically on the predisposition of the target cell itself. Redox catalysts, for instance, preferably target cells with a distinct sensitivity towards changes in an already disturbed redox balance and/or increased levels of reactive oxygen species. Indeed, certain transition metal, chalcogen and quinone agents may activate an antioxidant response in normal cells whilst at the same time triggering apoptosis in cancer cells with a different pre-existing "biochemical redox signature" and closer to the internal redox threshold. In pharmacy, catalysts therefore stand out as promising lead structures, as sensor/effector agents which are highly effective, fairly selective, active in catalytic, i.e., often nanomolar concentrations and also very flexible in their structural design. Redox ; Effector ; Chemistry ; Catalysis ; Context (archaeology) ; Biomolecule ; Small molecule ; Reactive oxygen species ; Rational design ; Antioxidant ; Biophysics ; Combinatorial chemistry ; Nanotechnology ; Biochemistry ; Cell biology ; Biology ; Materials science Click Chemistry and Applications ; Metal complexes synthesis and properties ; Synthesis and Biological Evaluation
Authors: Yunshu Gao | Genxiang Chen | Xiao Chen | Qian Zhang | Qiao Chen | Ce Zhang | Kai Tian | Zhongwei Tan | Chao Yu
Source: IEEE photonics journal (article, 2018-10-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Resolution Tunable Filter With Flexible Bandwidth and Power Attenuation Based on an LCoS Processor High-resolution optical filters and wavelength selective switches are the essential components in the current and next-generation dynamic optical networks. A high-resolution programmable filter for telecom application is proposed and experimentally demonstrated based on a 4 k phase-only liquid crystal on silicon (LCoS) spatial light modulator. The tuning resolution, bandwidth, and power attenuation for each wavelength channel can be modulated independently by remote software control. For each channel, the center wavelength is tuned in the step of 7.5 ± 1 pm and the 3 dB bandwidth achieves from 10 GHz to 3 THz. Furthermore, by multi-casting hologram design techniques for an LCoS, the power attenuation is adjusted from 0 dB to 30 dB with the step of 0.1 dB. The insertion loss is less than 6 dB across the entire C-band and 1.8 dB of it can be further improved by adopting an LCoS chip with smaller reflection loss. Liquid crystal on silicon ; Attenuation ; Materials science ; Bandwidth (computing) ; Optics ; Insertion loss ; Wavelength ; Optical filter ; Optoelectronics ; Channel spacing ; Computer science ; Wavelength-division multiplexing ; Telecommunications ; Liquid-crystal display ; Physics Liquid Crystal Research Advancements ; Optical Network Technologies ; Photonic and Optical Devices
Authors: Christian Kubitza | Carsten Ginsel | Florian Bittner | Antje Havemeyer | Bernd Clement | Axel J. Scheidig
Source: Acta Crystallographica Section F Structural Biology Communications (article, 2018-05-17)
Field: Materials Science — Materials Chemistry

T4 lysozyme-facilitated crystallization of the human molybdenum cofactor-dependent enzyme mARC The human mitochondrial amidoxime reducing component (hmARC) is a molybdenum cofactor-dependent enzyme that is involved in the reduction of a diverse range of N-hydroxylated compounds of either physiological or xenobiotic origin. In this study, the use of a fusion-protein approach with T4 lysozyme (T4L) to determine the structure of this hitherto noncrystallizable enzyme by X-ray crystallography is described. A set of four different hmARC-T4L fusion proteins were designed. Two of them contained either an N-terminal or a C-terminal T4L moiety fused to hmARC, while the other two contained T4L as an internal fusion partner tethered to the hmARC enzyme between two predicted secondary-structure elements. One of these internal fusion constructs could be expressed and crystallized successfully. The hmARC-T4L crystals diffracted to 1.7 Å resolution using synchrotron radiation and belonged to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with one molecule in the asymmetric unit. Initial attempts to solve the structure by molecular replacement using T4L did not result in electron-density distributions that were sufficient for model building and interpretation of the hmARC moiety. However, this study emphasizes the utility of the T4L fusion-protein approach, which can be used for the crystallization and structure determination of membrane-bound proteins as well as soluble proteins. Lysozyme ; Crystallization ; Cofactor ; Molybdenum ; Enzyme ; Chemistry ; Biochemistry ; Inorganic chemistry ; Organic chemistry Enzyme Structure and Function ; Amino Acid Enzymes and Metabolism ; Metalloenzymes and iron-sulfur proteins
Authors: Hadi H. Mohammad | Jack A. Connolly | Zhongshu Song | Joanne Hothersall | Paul R. Race | Christine L. Willis | Thomas J. Simpson | Peter J. Winn | Christopher M. Thomas
Source: ChemBioChem (article, 2018-01-24)
Field: Chemistry — Inorganic Chemistry

Fine Tuning of Antibiotic Activity by a Tailoring Hydroxylase in a Trans‐AT Polyketide Synthase Pathway The addition or removal of hydroxy groups modulates the activity of many pharmacologically active biomolecules. It can be integral to the basic biosynthetic factory or result from associated tailoring steps. For the anti-MRSA antibiotic mupirocin, removal of a C8-hydroxy group late in the biosynthetic pathway gives the active pseudomonic acid A. An extra hydroxylation, at C4, occurs in the related but more potent antibiotic thiomarinol A. We report here in vivo and in vitro studies that show that the putative non-haem-iron(II)/α-ketoglutaratedependent dioxygenase TmuB, from the thiomarinol cluster, 4-hydroxylates various pseudomonic acids whereas C8-OH, and other substituents around the tetrahydropyran ring, block enzyme action but not substrate binding. Molecular modelling suggested a basis for selectivity, but mutation studies had a limited ability to rationally modify TmuB substrate specificity. 4-Hydroxylation had opposite effects on the potency of mupirocin and thiomarinol. Thus, TmuB can be added to the toolbox of polyketide tailoring technologies for the in vivo generation of new antibiotics in the future. Hydroxylation ; Mupirocin ; Stereochemistry ; Chemistry ; Polyketide ; Polyketide synthase ; Enzyme ; Tetrahydropyran ; Nonribosomal peptide ; In vivo ; Biochemistry ; Biosynthesis ; Combinatorial chemistry ; Biology ; Bacteria ; Ring (chemistry) Metal-Catalyzed Oxygenation Mechanisms ; Synthesis and Catalytic Reactions ; Microbial Natural Products and Biosynthesis
Authors: Daniel Reker | Gonçalo J. L. Bernardes | Tiago Rodrigues
Source: ChemRxiv (preprint, 2018-11-02)
Field: Materials Science — Materials Chemistry

Evolving and Nano Data Enabled Machine Intelligence for Chemical Reaction Optimization &lt;p&gt;Optimizing reaction conditions is an essential routine in synthetic chemistry. However, selecting appropriate experiments remains tightly connected to expert chemistry knowledge. Here, to streamline the reaction yield optimization process and disconnect it from chemical intuition, we developed an adaptive machine intelligence to navigate multidimensional reaction conditions’ spaces. Our approach (LabMate.AI) employs an interpretable algorithm and requires only &lt;0.05% of all search space as input data. LabMate.AI optimizes many reaction parameters simultaneously, and uses minimal computational resources and time. We demonstrate how LabMate.AI can identify optimal conditions for a Ugi and a C–N cross-coupling reaction in a more efficient and faster manner than human experts, while affording reactivity insights. Our approach formalizes chemical intuition, and acquires expert chemistry knowledge autonomously, thereby providing an innovative framework towards informed and automated experiment selection. The results support machine learning for hastening experimental design, democratizing synthetic chemistry, and freeing chemists for non-routine tasks.&lt;/p&gt; Intuition ; Computer science ; Artificial intelligence ; Machine learning ; Chemical space ; Chemistry ; Biochemical engineering ; Engineering ; Cognitive science ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Austin D. Sendek | Ekin D. Cubuk | Evan R. Antoniuk | Gowoon Cheon | Yi Cui | Evan J. Reed
Source: arXiv (Cornell University) (preprint, 2018-08-07)
Field: Materials Science — Materials Chemistry

Machine learning-assisted discovery of many new solid Li-ion conducting materials We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the development of safe all-solid-state Li-ion batteries. With a predictive universal structure-property relationship for fast ion conduction not well understood, the search for new solid Li ion conductors has relied largely on trial-and-error computational and experimental searches over the last several decades. In this work, we perform a guided search of materials space with a machine learning (ML)-based prediction model for material selection and density functional theory molecular dynamics (DFT-MD) simulations for calculating ionic conductivity. These materials are screened from over 12,000 experimentally synthesized and characterized candidates with very diverse structures and compositions. When compared to a random search of materials space, we find that the ML-guided search is 2.7 times more likely to identify fast Li ion conductors, with at least a 45x improvement in the log-average of room temperature Li ion conductivity. The F1 score of the ML-based model is 0.50, 3.5 times better than the F1 score expected from completely random guesswork. In a head-to-head competition against six Ph.D. students working in the field, we find that the ML-based model doubles the F1 score of human experts in its ability to identify fast Li-ion conductors from atomistic structure with a thousand- fold increase in speed, clearly demonstrating the utility of this model for the research community. All conducting materials reported here lack transition metals and are predicted to exhibit low electronic conduction, high stability against oxidation, and high thermodynamic stability. Materials science ; Ion ; Nanotechnology ; Computer science ; Engineering physics ; Engineering ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Andre Loechte | Ole Gebert | Daniel Heming | Klaus T. Kallis | Peter Gloesekoetter
Source: Neural Computing and Applications (article, 2018-09-28)
Field: Chemistry — Electrochemistry

State estimation of zinc air batteries using neural networks Battery (electricity) ; Dielectric spectroscopy ; State of health ; Nonlinear system ; Artificial neural network ; Electrical impedance ; Voltage ; State of charge ; Computer science ; Equivalent circuit ; Set (abstract data type) ; State (computer science) ; Control theory (sociology) ; Electronic engineering ; Electrical engineering ; Algorithm ; Engineering ; Artificial intelligence ; Electrochemistry ; Power (physics) ; Chemistry ; Physics Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Shuang Wu | Shougen Zhao | Dafang Wu | Yunfeng Wang
Source: NeuroQuantology (article, 2018-05-27)
Field: Materials Science — Materials Chemistry

Constitutive Modelling for Restrained Recovery of Shape Memory Alloys Based on Artificial Neural Network This paper attempts to optimize the constitutive modelling of restrained recovery for the shape memory alloy (SMA). For this purpose, a backpropagation neural network (BPNN) model was developed to predict the restrained recovery of the SMA. The modelling data were collected from restrained recovery experiments on the SMA. Thanks to nonlinear function mapping and adaptation, the proposed model can learn the complete restrained recovery stress and temperature hysteresis of the SMA and predict the complete restrained recovery stress at different initial strains. The result analysis shows that the predicted data agree well with the experimental data. Compared to mathematical constitutive models, the proposed model is simple, cheap and convenient, and especially suitable for real-time applications. SMA* ; Artificial neural network ; Shape-memory alloy ; Constitutive equation ; Nonlinear system ; Computer science ; Backpropagation ; Hysteresis ; Stress (linguistics) ; Experimental data ; Artificial intelligence ; Algorithm ; Structural engineering ; Engineering ; Mathematics ; Finite element method Shape Memory Alloy Transformations
Authors: M. Gokhan Habiboglu | Orkid Coskuner‐Weber
Source: Zeitschrift für Physikalische Chemie (article, 2018-10-04)
Field: Chemistry — Organic Chemistry

Quantum Chemistry Meets Deep Learning for Complex Carbohydrate and Glycopeptide Species I Abstract Carbohydrate complexes are crucial in many various biological and medicinal processes. The impacts of N-acetyl on the glycosidic linkage flexibility of methyl β -D-glucopyranose, and of the glycoamino acid β -D-glucopyranose-asparagine are poorly understood at the electronic level. Furthermore, the effect of D- and L-isomers of asparagine in the complexes of N-acetyl- β -D-glucopyranose-(L)-asparagine and N-acetyl- β -D-glucopyranose-(D)-asparagine is unknown. In this study, we performed density functional theory calculations of methyl β -D-glucopyranose, methyl N-acetyl- β -D-glucopyranose, and of glycoamino acids β -D-glucopyranose-asparagine, N-acetyl- β -D-glucopyranose-(L)-asparagine and N-acetyl- β -D-glucopyranose-(D)-asparagine for studying their linkage flexibilities, total solvated energies, thermochemical properties and intra-molecular hydrogen bond formations in an aqueous solution environment using the COnductor-like Screening MOdel (COSMO) for water. We linked these density functional theory calculations to deep learning via estimating the total solvated energy of each linkage torsional angle value. Our results show that deep learning methods accurately estimate the total solvated energies of complex carbohydrate and glycopeptide species and provide linkage flexibility trends for methyl β -D-glucopyranose, methyl N-acetyl- β -D-glucopyranose, and of glycoamino acids β -D-glucopyranose-asparagine, N-acetyl- β -D-glucopyranose-(L)-asparagine and N-acetyl- β -D-glucopyranose-(D)-asparagine in agreement with density functional theory results. To the best of our knowledge, this study represents the first application of density functional theory along with deep learning for complex carbohydrate and glycopeptide species in an aqueous solution medium. In addition, this study shows that a few thousands of optimization frames from DFT calculations are enough for accurate estimations by deep learning tools. Asparagine ; Chemistry ; Density functional theory ; Glycosidic bond ; Aqueous solution ; Glycopeptide ; Conformational isomerism ; Carbohydrate ; Amino acid ; Computational chemistry ; Stereochemistry ; Molecule ; Organic chemistry ; Biochemistry Carbohydrate Chemistry and Synthesis ; Probiotics and Fermented Foods ; Glycosylation and Glycoproteins Research
Authors: Elias T. Silva | Francisco José Alves de Aquino | D.A. Silva | Ajalmar R. Rocha Neto | Kaio Jonathas Gurgel | Antonio É. R. M. de Oliveira | André Luiz Carneiro de Araújo
Source: IEEE Latin America Transactions (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions The occurrence of corona effect is an important indication that an insulator probably has malfunction. Thus, the corona detection can help to diagnose problems in insulators, which might cause serious damage to the power distribution system, such as the sudden interruption of service. Machine learning techniques are used to classifying patterns into categories. This paper evaluated the application of MLP (multilayer perceptron) and SVM (support vector machines) for corona detection in high voltage polymeric insulators using ultrasonic emissions. To perform the capture of these data, a specific electronic circuit was developed in order to capture the ultrasonic signals tuned in the 40 kHz. The results indicate that both methods offer high generalizability and looks to be promising techniques for this type of problem, using ultrasonic frequencies as the data source. Insulator (electricity) ; Ultrasonic sensor ; Corona (planetary geology) ; Support vector machine ; Corona discharge ; Perceptron ; Voltage ; Computer science ; Generalizability theory ; Materials science ; Acoustics ; Electrical engineering ; Artificial neural network ; Artificial intelligence ; Engineering ; Optoelectronics ; Physics High voltage insulation and dielectric phenomena ; Traffic Prediction and Management Techniques ; Power Transformer Diagnostics and Insulation