jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emilio Scalise
Source: Semiconductor Science and Technology (article, 2019-10-30)
Field: Materials Science — Materials Chemistry

Tailoring the electronic properties of semiconducting nanocrystal-solids Abstract An overview of the recent progress made in the synthesis of colloidal semiconducting nanocrystals (NCs), with particular emphasis on applications for solar cell materials is provided. Peculiar phenomena in NCs, such as multiple exciton generation and intermediate-band absorption are described as important mechanisms to improve the efficiency of the third-generation solar cells. The state of art and challenges to exploit these effects in NCs based materials are highlighted. We focus on two complementary approaches that are very promising: the synthesis of core/shell NCs and the incorporation of these NCs into semiconducting matrices, thus creating a fully inorganic quantum dot solid. We also show two examples of inorganic quantum dot solids, one using Si or Ge NCs and another employing InAs or InP NCs. For the latter, a joint experimental and theoretical study is reviewed, showing how quantum dot solids introduce new concepts and need for a new way of thinking different than that just summing up the effects and properties of NCs and the semiconducting hosting matrix. Nanocrystal ; Quantum dot ; Nanotechnology ; Solar cell ; Materials science ; Multiple exciton generation ; Exciton ; Absorption (acoustics) ; Nanoparticle ; Electronic structure ; Optoelectronics ; Chemistry ; Physics ; Quantum mechanics ; Computational chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanowire Synthesis and Applications
Authors: E. Freisinger | Roland K. O. Sigel
Source: CHIMIA International Journal for Chemistry (article, 2019-03-20)
Field: Chemistry — Physical and Theoretical Chemistry

The Bioinorganic Periodic Table Life depends on metals. While carbon, in terms of abundance and versatility, is considered THE element of life, the vast variety and diversity of the chemistry taking place in living organisms could not be achieved without metal ions. More than twenty metals are found in the human body, most of them being essential, some beneficial, and for others it is still unknown what role they might fulfil in a living cell. Here we give a short introduction into the bioinorganic world of the periodic table, providing just a few examples of key metals for life and aiming to give a flavour to gain further insights into this exciting field of inorganic chemistry at the intersection to the life sciences. Bioinorganic chemistry ; Chemistry ; Periodic table ; Table (database) ; Nanotechnology ; Materials science ; Biochemistry ; Computer science ; Organic chemistry History and advancements in chemistry ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Н. Ж. Мамардашвили | Yu. B. Ivanova | Н. В. Чижова
Source: Macroheterocycles (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Synthesis and Acid-base Properties of Isomeric Tetrachlorooctabromo- and Tetrabromooctachlorotetraphenylporphyrins porphyrin chlorination with excess of the thionyl chloride and Co (II)-5,10,15,20-tetra(4-chlorophenyl)porphyrin bromination with N-bromosuccinimide were investigated. Co (II)-2,3,7,8,12,13,17,18-octabromo-5,10,15,20-tetra(chlorophenyl)porphyrin and Co(II)-2,3,7,8,12,13,17,18-octachloro-5,10,15,20-tetra(4-bromophenyl)porphyrin were synthesized and identified by UV-Vis, 1 H NMR spectroscopy and mass-spectrometry. When the halogen substituted Co(II) porphyrins were treated with a chloric and sulfuric acids mixture, corresponding porphyrin-ligands were obtained. The acid-base properties of the 2,3,7,8,12,13,17,18-octabromo-5,10,15,20-tetra(4-chlorophenyl)porphyrin and 2,3,7,8,12,13,17,18-octachloro-5,10,15,20-tetra(4-bromophenyl) porphyrin in acetonitrile were studied. Acidity and basicity constants of the synthesized porphyrin ligands were determined and the concentration intervals for their ionized forms existence were established. Base (topology) ; Chemistry ; Mathematics Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Molecular Sensors and Ion Detection
Authors: K. Jagajjanani Rao | Tarangini Korumilli
Source: Micro & Nano Letters (article, 2019-08-02)
Field: Chemistry — Organic Chemistry

Au nanoparticles by instant green chemicals and optimising key synthesis parameters using Taguchi method In the recent years, functionalised Au nanoparticles using eco‐friendly chemical routes are under the focus for developing several technological applications. However, a simple, reproducible, and controlled synthesis of high‐quality nanoparticles is challenging in these approaches. In this work, readily available green chemicals were utilised and the usefulness of and Taguchi approach for optimisation of synthesis procedure was designed for the synthesis of possible lower size Au nanoparticles of size 93 ± 7 nm. L16 orthogonal array, by which the influence of four factors in four levels may well be tested, was chosen. Analysis of variance was done on the attained results and optimum condition suggested by statistical estimations was tested through a verification test. The identified key parameters for synthesising lower size Au nanoparticles are reducing agent (curcumin + cinnamic acid + citric acid), additive (zero), sonication time (30 min), and temperature (50°C) after performing the optimisation technique. Furthermore, this study also provides a selective approach to obtain different shape/shape Au nano to colloidal structures through different trail selection within the Taguchi experimental array. Instant ; Taguchi methods ; Key (lock) ; Nanoparticle ; Nanotechnology ; Process engineering ; Biochemical engineering ; Computer science ; Manufacturing engineering ; Materials science ; Engineering ; Chemistry ; Computer security ; Machine learning ; Food science Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Tanay Paul | Jayashree Saha
Source: Physical Review Research (article, 2019-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computer simulation study of novel chiral liquid crystal phases The physical origin for the stabilization of novel chiral bilayer phase has been investigated using coarse-grained computer simulation. The study reveals that suitable combination of dipolar, chiral and dispersive interactions can give rise to various interesting chiral liquid crystal phases. Liquid crystal ; Bilayer ; Chemical physics ; Phase (matter) ; Materials science ; Dipole ; Crystal (programming language) ; Crystallography ; Chemistry ; Computer science ; Organic chemistry ; Optoelectronics ; Membrane Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Characterization and Applications of Magnetic Nanoparticles
Authors: Clayton R. Mulvihill
Source: OakTrust (Texas A&M University Libraries) (article, 2019-07-03)
Field: Chemistry — Spectroscopy

H2O LASER ABSORPTION AND OH* CHEMILUMINESCENCE MEASUREMENTS OF H2-NO2 OXIDATION IN A SHOCK TUBE The mitigation of NOx pollutants continues to be a matter of environmental and economic interest in the gas-turbine industry, and an improved understanding of fundamental NOx chemical kinetics has led to reductions in NOx emissions. However, the exhaust gas recirculation (EGR) method necessitates an accurate understanding of NOx/fuel interactions, and few experimental data with NOx as the sole oxidizer exist. New measurements of this kind would provide unique target data for the continuing design and optimization of NOx chemical kinetics mechanisms. To this end, new combustion data in the H2-NOv2 system were acquired. Experiments were performed behind reflected shock waves near 1 atm and at temperatures between 917 and 2003 K. Fuel-lean, near-stoichiometric, and fuel-rich mixtures of H2-NOv2 diluted in approximately 99 percent Ar were studied. \nA laser absorption diagnostic near 1.39 µm measured H2O time histories, while an emission diagnostic near 307 nm measured exited-state OH (OH asterisk) time histories. Kinetic modeling of the H2O data revealed the species HONO and NO3 are important in the H2vO reaction pathways. Newer mechanisms predicted the experimental H2O profiles quite well at fuel-lean and high-temperature conditions, but discrepancies persisted at fuel-rich and low-temperature conditions despite variation of the most-sensitive reactions. The kinetic models were unable to predict the experimental OH asterisk profile shapes with any sort of accuracy, suggesting the need for a new OH asterisk-forming reaction. By fitting the OH* profile shapes and considering exothermicity, the reaction NH + NOv2 ⇄ N2vO + OH asterisk was identified, and a tentative rate constant of kv13 = 5.0×10^^16·exp(- 40,000/RT) was proposed (units of [cal], [mol], [cm^3 ], [s]). Chemiluminescence ; Shock tube ; Absorption (acoustics) ; Laser ; Shock (circulatory) ; Tube (container) ; Photochemistry ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Optics ; Chromatography ; Shock wave ; Medicine ; Physics ; Thermodynamics ; Composite material ; Internal medicine Spectroscopy and Laser Applications ; Industrial Gas Emission Control ; Atmospheric and Environmental Gas Dynamics
Authors: Philippe Schwaller | Riccardo Petraglia | Valerio Zullo | Vishnu H Nair | Rico Andreas Haeuselmann | Riccardo Pisoni | Costas Bekas | Anna Iuliano | Teodoro Laino
Source: ChemRxiv (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Predicting Retrosynthetic Pathways Using a Combined Linguistic Model and Hyper-Graph Exploration Strategy We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosyn- thesis route planning without human intervention. The single-step ret- rosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is con- structed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and aca- demic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.Available on IBM RXN for Chemistry: https://rxn.res.ibm.com. Retrosynthetic analysis ; Computer science ; Transformer ; Artificial intelligence ; IBM ; Graph ; Bayesian probability ; Machine learning ; Theoretical computer science ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Л. Л. Гогин | Е. Г. Жижина | З. П. Пай
Source: Modern Research in Catalysis (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

One-Pot Process of Naphthoquinones Synthesis from Hydroquinone in the Presence of Solutions of Mo-V-P Heteropolyacids as Bifunctional Catalysts 1,4-Naphthoquinones (NQ) is a class of organic compounds with a number of important properties. However, the existing methods of their synthesis either give a low NQ yield or multistage and require a lot of time. In the present work new one-pot process of NQ synthesis from hydroquinone (HQ) and substituted 1,3-dienes at room temperature is proposed. The solution of heteropolyacid H7PMo8V4O40 (HPA-4) is used as a bifunctional (acid and redox) catalyst for this process. The influence of a number of factors on key process parameters has been studied. It is demonstrated that unsubstituted and a number of alkyl substituted NQ with the yields of 50% - 80% and purity of 92% - 99% can be obtained by this method. The results obtained allow to significantly simplify the synthesis of NQ directly from HQ in comparison with the described similar methods. Bifunctional ; Hydroquinone ; Catalysis ; Yield (engineering) ; Chemistry ; Process (computing) ; Alkyl ; Combinatorial chemistry ; Redox ; Organic chemistry ; Materials science ; Computer science Polyoxometalates: Synthesis and Applications ; Bioactive Compounds and Antitumor Agents ; Organic Chemistry Cycloaddition Reactions
Authors: Prabhjyot Kaur | Adam Duong | S.K. Chawla | Sukhleen Bindra Narang | Sher Singh Meena
Source: SN Applied Sciences (article, 2019-09-19)
Field: Materials Science — Materials Chemistry

Effect of precursors on the structural, magnetic, dielectric, microwave and electromagnetic properties of Co–Zr doped nanocrystalline strontium hexaferrites synthesized via sol–gel method Materials science ; Nanocrystalline material ; Dielectric ; Sol-gel ; Analytical Chemistry (journal) ; Ferrite (magnet) ; Ethylene glycol ; Strontium ; Microwave ; Dopant ; Doping ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry ; Optoelectronics Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Magneto-Optical Properties and Applications
Authors: Sereina Riniker | Shuzhe Wang | Patrick Bleiziffer | Lennard Böselt | Carmen Esposito
Source: CHIMIA International Journal for Chemistry (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning with and for Molecular Dynamics Simulations From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up, interpretation, as well as accuracy of MD simulations. Computer science ; Artificial intelligence ; Machine learning ; Set (abstract data type) ; Molecular dynamics ; Cluster analysis ; Interpretation (philosophy) ; Simple (philosophy) ; Field (mathematics) ; Artificial neural network ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Mengshan Li | Suyun Lian | Fan Wang | Yanying Zhou | Bingsheng Chen | Lixin Guan | Wu Yan
Source: Scientific Reports (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value E<sub>c</sub> of absorption energy (B3LYP/STO-3G), molecular electron number N<sub>e</sub>, oscillator strength O<sub>s</sub>, number of double bonds N<sub>db</sub>, total number of atoms N<sub>a</sub>, number of hydrogen atoms N<sub>h</sub>, number of carbon atoms N<sub>c</sub>, and number of nitrogen atoms N<sub>N</sub>; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc. Particle swarm optimization ; Absorption (acoustics) ; Artificial neural network ; Mean squared error ; Linear regression ; Correlation coefficient ; Algorithm ; Stability (learning theory) ; Materials science ; Biological system ; Computer science ; Statistical physics ; Physics ; Mathematics ; Artificial intelligence ; Machine learning ; Statistics ; Optics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Zhe Wu | Anh Tran | Yi Ren | Cory S. Barnes | Siyao Chen | Panagiotis D. Christofides
Source: IFAC-PapersOnLine (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Model Predictive Control of Distributed Chemical Processes This work proposes a general framework for linking of a state-of-the-art computational fluid dynamics (CFD) solver, ANSYS Fluent, and other computing platforms using the lock synchronization mechanism in an effort to extend the utilities of CFD solvers from strictly modeling and design to also control and optimization applications. Specifically, phthalic anhydride (PA) synthesis is chosen for this investigation because of its industrial significance and its extreme high exothermicity. Initially, a high-fidelity two-dimensional axisymmetric heterogeneous CFD model for an industrial-scale FBR is developed in ANSYS Fluent. Next, the CFD model is used to explore a wide operating regime of the FBR to create a database, from which recurrent neural network and ensemble learning techniques are used to derive a homogeneous ensemble regression model using a state-of-the-art application program interface. Then, a model predictive control (MPC) formulation that is designed to drive the process performance to the desired set-point and to avoid catalyst deactivation is developed using the ensemble regression model. Subsequently, the CFD model, the ensemble regression model and the MPC are combined to create a closed-loop system by linking ANSYS Fluent to SciPy via a message-passing interface (MPI) with lock synchronization mechanism. Finally, the simulation data generated by the closed-loop system are used to demonstrate the robustness and effectiveness of the proposed approach. Computer science ; Computational fluid dynamics ; Model predictive control ; Solver ; Fluent ; Robustness (evolution) ; Simulation ; Control engineering ; Artificial intelligence ; Computer simulation ; Engineering ; Control (management) Catalytic Processes in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Tiberiu Stan | Zachary Thompson | Peter W. Voorhees
Source:  (article, 2019-09-10)
Field: Materials Science — Materials Chemistry

Building towards a universal neural network to segment large materials science imaging datasets Segmentation of large images can be one of the most time-consuming steps in the analysis of materials science datasets. Convolutional neural networks (NNs) have been shown to reduce segmentation time compared to manual techniques, but training a new NN is often required for each dataset. We show that simply combining NN training datasets does not necessarily lead to a NN capable of segmenting multiple types of images. In the present study, we first show that SegNet-based neural networks (NNs) can be trained to accurately segment Al-Zn x-ray computed tomography and Pb-Sn serial sectioning images. Applying the Al-Zn NN to the Pb-Sn test image led to misclassified smudges as dendrites, and misclassified speckles as background. Applying the Pb-Sn NN to the Al-Zn test image was unsuccessful, likely because the Al-Zn dendrites had a higher luminance than the Pb-Sn dendrites. The Mix NN (trained using the combined Al-Zn and Pb-Sn datasets) was better at segmenting the Pb-Sn test image than the Al-Zn test image. This is likely because the Pb-Sn training dataset contained ~4.5 times as many dendrite pixels as the Al-Zn training dataset, thus the Mix NN was over-tuned to identify Pb-Sn dendrites. Simply combining the training datasets was overall detrimental to NN performance, but assigning different classes to the Al-Zn and Pb-Sn dendrites may lead to enhanced performance in the future. These findings serve as guidelines in the quest to develop a universal NN for segmentation of large materials science datasets. Computer science ; Artificial neural network ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Mineral Processing and Grinding
Authors: James McDonagh | Ardita Shkurti | David J. Bray | Richard L. Anderson | Edward O. Pyzer‐Knapp
Source: ChemRxiv (preprint, 2019-07-17)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields This work demonstrates the use of open literature data to force field paramterization via a novel approach applying Bayesian optimization. We have selected Dissipative Particle Dynamics (DPD) as the simulation method in this proof-of-concept work. Dissipative particle dynamics ; Force field (fiction) ; Computer science ; Work (physics) ; Dissipative system ; Bayesian optimization ; Bayesian probability ; Molecular dynamics ; Field (mathematics) ; Proof of concept ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies