jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Consuelo Álvarez‐Galván | Mayra Melian | Laura Ruiz-Matas | José L. Eslava | R.M. Navarro | Mahdi Ahmadi | Beatriz Roldán Cuenya | J.L.G. Fierro
Source: Frontiers in Chemistry (article, 2019-03-13)
Field: Materials Science — Materials Chemistry

Partial Oxidation of Methane to Syngas Over Nickel-Based Catalysts: Influence of Support Type, Addition of Rhodium, and Preparation Method There is great economic incentive in developing efficient catalysts to produce hydrogen or syngas by catalytic partial oxidation of methane (CPOM) since this is a much less energy-intensive reaction than the highly endothermic methane steam reforming reaction, which is the prominent reaction in industry. Herein, we report the catalytic behavior of nickel-based catalysts supported on different oxide substrates (Al<sub>2</sub>O<sub>3</sub>, CeO<sub>2</sub>, La<sub>2</sub>O<sub>3</sub>, MgO, and ZrO<sub>2</sub>) synthesized via wet impregnation and solid-state reaction. Furthermore, the impact of Rh doping was investigated. The catalysts have been characterized by X-ray diffraction, N<sub>2</sub> adsorptiondesorption at -196°C, temperature-programmed reduction, X-ray photoelectron spectroscopy, O<sub>2</sub>-pulse chemisorption, transmission electron microscopy, and Raman spectroscopy. Supported Ni catalysts were found to be active for CPOM but can suffer from fast deactivation caused by the formation of carbon deposits as well as via the sintering of Ni nanoparticles (NPs). It has been found that the presence of Rh favors nickel reduction, which leads to an increase in the methane conversion and yield. For both synthesis methods, the catalysts supported on alumina and ceria show the best performance. This could be explained by the higher surface area of the Ni NPs on the alumina surface and presence of oxygen vacancies in the CeO<sub>2</sub> lattice, which favor the proportion of oxygen adsorbed on defect sites. The catalysts supported on MgO suffer quick deactivation due to formation of a NiO/MgO solid solution, which is not reducible under the reaction conditions. The low level of carbon formation over the catalysts supported on La<sub>2</sub>O<sub>3</sub> is ascribed to the very high dispersion of the nickel NPs and to the formation of lanthanum oxycarbonate, through which carbon deposits are gasified. The catalytic behavior for catalysts with ZrO<sub>2</sub> as support depends on the synthesis method; however, in both cases, the catalysts undergo deactivation by carbon deposits. Syngas ; Rhodium ; Partial oxidation ; Nickel ; Methane ; Catalysis ; Chemistry ; Inorganic chemistry ; Chemical engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Luigi Consolino | Malik Nafa | Francesco Cappelli | Katia Garrasi | Francesco P. Mezzapesa | Lianhe Li | A. G. Davies | E. H. Linfield | Miriam S. Vitiello | Paolo De Natale | Saverio Bartalini
Source: Nature Communications (article, 2019-07-03)
Field: Chemistry — Spectroscopy

Fully phase-stabilized quantum cascade laser frequency comb Miniaturized frequency comb sources across hard-to-access spectral regions, i.e. mid- and far-infrared, have long been sought. Four-wave-mixing based Quantum Cascade Laser combs (QCL-combs) are ideal candidates, in this respect, due to the unique possibility to tailor their spectral emission by proper nanoscale design of the quantum wells. We demonstrate full-phase-stabilization of a QCL-comb against the primary frequency standard, proving independent and simultaneous control of the two comb degrees of freedom (modes spacing and frequency offset) at a metrological level. Each emitted mode exhibits a sub-Hz relative frequency stability, while a correlation analysis on the modal phases confirms the high degree of coherence in the device emission, over different power-cycles and over different days. The achievement of fully controlled, phase-stabilized QCL-comb emitters proves that this technology is mature for metrological-grade uses, as well as for an increasing number of scientific and technological applications. Frequency comb ; Quantum cascade laser ; Cascade ; Metrology ; Coherence (philosophical gambling strategy) ; Laser ; Quantum ; Optics ; Interferometry ; Frequency offset ; Physics ; Optoelectronics ; Spectral density ; Offset (computer science) ; Quantum metrology ; Materials science ; Quantum network ; Quantum information ; Telecommunications ; Computer science ; Chemistry ; Quantum mechanics Spectroscopy and Laser Applications ; Advanced Fiber Laser Technologies ; Laser Design and Applications
Authors: Anjan Barman | Sohini Kar‐Narayan | Devajyoti Mukherjee
Source: Advanced Materials Interfaces (article, 2019-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Caloric Effects in Perovskite Oxides Abstract Perovskite oxides show an amazing diversity of electronic and magnetic properties along with a myriad of structural variants and phase transitions. Large thermal changes may be driven near the ferroic phase transitions in perovskite oxides using magnetic, electric, and stress fields to manipulate conjugate order parameters. The ensuing magnetocaloric, electrocaloric, and mechanocaloric effects can be utilized for environment‐friendly and high‐efficiency solid‐state cooling applications. In this review the details of these caloric effects in perovskite oxides both from a chronological perspective and from the viewpoint of the recent advances in multiple caloric phenomena are described. The authors highlight the role of interfaces in oxide thin films for the different caloric effects and address some of the outstanding challenges for the fundamental understanding and practical implementation of perovskite oxides in solid state refrigeration. Perovskite (structure) ; Materials science ; Magnetic refrigeration ; Oxide ; Refrigeration ; Caloric theory ; Phase transition ; Solid-state ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Chemical engineering ; Magnetic field ; Thermodynamics ; Magnetization ; Metallurgy ; Physics Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides
Authors: Tanja Dimitrov | Christoph Kreisbeck | Jill Becker | Alán Aspuru‐Guzik | Semion K. Saikin
Source: ACS Applied Materials & Interfaces (article, 2019-03-25)
Field: Materials Science — Materials Chemistry

Autonomous Molecular Design: Then and Now The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Such systems learn through every single experiment in an analogy to a human scientific expert. While the general idea of an autonomous system for molecular synthesis and characterization has been around for a while, its implementations for the materials sciences are sparse. Here we provide an overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in. Computer science ; Focus (optics) ; Automation ; Implementation ; Artificial intelligence ; Characterization (materials science) ; Deep learning ; Analogy ; Data science ; Molecular machine ; Human–computer interaction ; Nanotechnology ; Machine learning ; Software engineering ; Materials science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lucas Lindsay | Ankita Katre | Andrea Cepellotti | Natalio Mingo
Source: Journal of Applied Physics (article, 2019-08-05)
Field: Materials Science — Materials Chemistry

Perspective on <i>ab initio</i> phonon thermal transport Coupling of the Peierls-Boltzmann equation with density functional theory paved the way for predictive thermal materials discovery and a variety of new physical insights into vibrational transport behaviors. Rapid theoretical and numerical developments have generated a wealth of thermal conductivity data and understanding of a wide variety of materials—1D, 2D, and bulk—for thermoelectric and thermal management applications. Nonetheless, modern ab initio descriptions of phonon thermal transport face challenges regarding the effects of defects, disorder, structural complexity, strong anharmonicity, quasiparticle couplings, and time and spatially varying perturbations. Highlighting recent research on these issues, this perspective explores opportunities to expand current ab initio phonon transport techniques beyond the paradigm of weakly perturbed crystals, to the wider variety of materials possible. Recent developments in phonon-defect interactions, complexity, disorder and anharmonicity, hydrodynamic transport, and the rising roles of molecular dynamics simulations, high throughput, and machine learning tools are included in this perspective. As more sophisticated theoretical and computational methods continue to advance thermal transport predictions, novel vibrational physics and thermally functional materials will be discovered for improved energy technologies. Anharmonicity ; Phonon ; Ab initio ; Thermal conductivity ; Boltzmann equation ; Variety (cybernetics) ; Density functional theory ; Statistical physics ; Thermoelectric materials ; Physics ; Condensed matter physics ; Computer science ; Quantum mechanics ; Artificial intelligence Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Wojciech Jaworski | Sara Szymkuć | Barbara Mikulak-Klucznik | Krzysztof Piecuch | Tomasz Klucznik | Michał Kaźmierowski | Jan M. Rydzewski | Anna Gambin | Bartosz A. Grzybowski
Source: Nature Communications (article, 2019-03-29)
Field: Materials Science — Materials Chemistry

Automatic mapping of atoms across both simple and complex chemical reactions Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer's help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible "intermediate" atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and - above all - machine extraction of reaction rules underlying modern synthesis-planning programs. Computer science ; Template ; Simple (philosophy) ; Theoretical computer science ; Substructure ; Atom (system on chip) ; Identification (biology) ; Key (lock) ; Graph ; Algorithm ; Data mining ; Programming language ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graph Theory and Algorithms
Authors: Amir Muhammad | Anwar‐ul‐Haq Ali Shah | Salma Bilal | Gul Rahman
Source: Materials (article, 2019-05-30)
Field: Materials Science — Polymers and Plastics

Basic Blue Dye Adsorption from Water Using Polyaniline/Magnetite (Fe3O4) Composites: Kinetic and Thermodynamic Aspects Owing to its exciting physicochemical properties and doping-dedoping chemistry, polyaniline (PANI) has emerged as a potential adsorbent for removal of dyes and heavy metals from aqueous solution. Herein, we report on the synthesis of PANI composites with magnetic oxide (Fe<sub>3</sub>O<sub>4</sub>) for efficient removal of Basic Blue 3 (BB3) dye from aqueous solution. PANI, Fe<sub>3</sub>O<sub>4</sub>, and their composites were characterized with several techniques and subsequently applied for adsorption of BB3. Effect of contact time, initial concentration of dye, pH, and ionic strength on adsorption behavior were systematically investigated. The data obtained were fitted into Langmuir, Frundlich, Dubbanin-Rudiskavich (D-R), and Tempkin adsorption isotherm models for evaluation of adsorption parameters. Langmuir isotherm fits closely to the adsorption data with R<sup>2</sup> values of 0.9788, 0.9849, and 0.9985 for Fe<sub>3</sub>O<sub>4</sub>, PANI, and PANI/Fe<sub>3</sub>O<sub>4</sub> composites, respectively. The maximum amount of dye adsorbed was 7.474, 47.977, and 78.13 mg/g for Fe<sub>3</sub>O<sub>4</sub>, PANI, and PANI/Fe<sub>3</sub>O<sub>4</sub> composites, respectively. The enhanced adsorption capability of the composites is attributed to increase in surface area and pore volume of the hybrid materials. The adsorption followed pseudo second order kinetics with R<sup>2</sup> values of 0.873, 0.979, and 0.999 for Fe<sub>3</sub>O<sub>4</sub>, PANI, and PANI/Fe<sub>3</sub>O<sub>4</sub> composites, respectively. The activation energy, enthalpy, Gibbs free energy changes, and entropy changes were found to be 11.14, -32.84, -04.05, and -0.095 kJ/mol for Fe<sub>3</sub>O<sub>4</sub>, 11.97, -62.93, -07.78, and -0.18 kJ/mol for PANI and 09.94, -74.26, -10.63, and -0.210 kJ/mol for PANI/Fe<sub>3</sub>O<sub>4</sub> respectively, which indicate the spontaneous and exothermic nature of the adsorption process. Magnetite ; Polyaniline ; Adsorption ; Kinetic energy ; Materials science ; Composite material ; Chemical engineering ; Chemistry ; Polymer ; Physical chemistry ; Metallurgy Conducting polymers and applications ; Adsorption and biosorption for pollutant removal ; Electrochemical sensors and biosensors
Authors: Aysun Çapcı | Mélanie M. Lorion | Hui Wang | Nina Simon | Maria Leidenberger | Mariana da Cruz Borges Silva | Diogo Rodrigo Magalhães Moreira | Yongping Zhu | Yuqing Meng | Jia Yun Chen | Yew Mun Lee | Oliver Friedrich | Barbara Kappes | Jigang Wang | Lutz Ackermann | Svetlana B. Tsogoeva
Source: Angewandte Chemie International Edition (article, 2019-07-10)
Field: Chemistry — Organic Chemistry

Artemisinin–(Iso)quinoline Hybrids by C−H Activation and Click Chemistry: Combating Multidrug‐Resistant Malaria A substantial challenge worldwide is emergent drug resistance in malaria parasites against approved drugs, such as chloroquine (CQ). To address these unsolved CQ resistance issues, only rare examples of artemisinin (ART)-based hybrids have been reported. Moreover, protein targets of such hybrids have not been identified yet, and the reason for the superior efficacy of these hybrids is still not known. Herein, we report the synthesis of novel ART-isoquinoline and ART-quinoline hybrids showing highly improved potencies against CQ-resistant and multidrug-resistant P. falciparum strains (EC<sub>50</sub> (Dd2) down to 1.0 nm; EC<sub>50</sub> (K1) down to 0.78 nm) compared to CQ (EC<sub>50</sub> (Dd2)=165.3 nm; EC<sub>50</sub> (K1)=302.8 nm) and strongly suppressing parasitemia in experimental malaria. These new compounds are easily accessible by step-economic C-H activation and copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) click reactions. Through chemical proteomics, putatively hybrid-binding protein targets of the ART-quinolines were successfully identified in addition to known targets of quinoline and artemisinin alone, suggesting that the hybrids act through multiple modes of action to overcome resistance. Artemisinin ; Quinoline ; Click chemistry ; Isoquinoline ; Cycloaddition ; Hybrid ; Chemistry ; Multiple drug resistance ; Plasmodium falciparum ; Combinatorial chemistry ; Malaria ; Drug resistance ; Stereochemistry ; Biology ; Biochemistry ; Catalysis ; Genetics ; Botany ; Organic chemistry ; Immunology Synthesis and Catalytic Reactions ; Malaria Research and Control ; Catalytic C–H Functionalization Methods
Authors: Marcus M. Noack | Kevin G. Yager | Masafumi Fukuto | Gregory S. Doerk | Ruipeng Li | James A. Sethian
Source: Scientific Reports (article, 2019-08-14)
Field: Materials Science — Materials Chemistry

A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems-that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. Here, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We validate the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments. Computer science ; Context (archaeology) ; Kriging ; Data point ; Set (abstract data type) ; Algorithm ; Surrogate model ; Data mining ; Machine learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Scientific Computing and Data Management
Authors: Michael W. Mara | David S. Tatum | Anne Marie March | Gilles Doumy | Evan G. Moore | Kenneth N. Raymond
Source: Journal of the American Chemical Society (article, 2019-06-23)
Field: Materials Science — Materials Chemistry

Energy Transfer from Antenna Ligand to Europium(III) Followed Using Ultrafast Optical and X-ray Spectroscopy A series of highly luminescent europium(III) complexes which exhibit photoluminescence from the Eu(III) center following energy transfer from the UV absorbing organic sensitizer have been investigated using a combination of ultrafast optical transient absorption and Eu L3 X-ray transient absorption techniques. We have previously demonstrated that the latter can be used as a signature of 4f-4f excitation responsible for the photoluminescence in these Eu(III) coordination complexes, but the long time scale of the earlier measurements did not allow direct observation of the ligand-to-metal energy transfer step, preventing a determination of the sensitization mechanism. Here, we provide the first direct experimental verification that Dexter electron exchange from the ligand triplet state is the dominant energy transfer mechanism in these photoluminescent systems. Moreover, the optical transient absorption results obtained herein imply that energy transfer for all three compounds has near unity yield, regardless of differences in the sensitization efficiencies, suggesting that the variations in the sensitization efficiencies are determined almost entirely by differences in the ligand-centered intersystem crossing rates. The implications for the rational design of more effective photoluminescent lanthanide complexes are discussed. Chemistry ; Europium ; Ultrashort pulse ; Spectroscopy ; Energy transfer ; Ligand (biochemistry) ; Antenna (radio) ; Antenna effect ; Photochemistry ; Optics ; Chemical physics ; Luminescence ; Ion ; Telecommunications ; Laser ; Physics ; Organic chemistry Lanthanide and Transition Metal Complexes ; Radioactive element chemistry and processing ; Advanced MRI Techniques and Applications
Authors: Silvia Amabilino | Lars Andersen Bratholm | Simon J. Bennie | Alain C. Vaucher | Markus Reiher | David R. Glowacki
Source: The Journal of Physical Chemistry A (article, 2019-03-20)
Field: Materials Science — Materials Chemistry

Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality While the primary bottleneck to a number of computational workflows was not so long ago limited by processing power, the rise of machine learning technologies has resulted in an interesting paradigm shift, which places increasing value on issues related to data curation-that is, data size, quality, bias, format, and coverage. Increasingly, data-related issues are equally as important as the algorithmic methods used to process and learn from the data. Here we introduce an open-source graphics processing unit-accelerated neural network (NN) framework for learning reactive potential energy surfaces (PESs). To obtain training data for this NN framework, we investigate the use of real-time interactive ab initio molecular dynamics in virtual reality (iMD-VR) as a new data curation strategy that enables human users to rapidly sample geometries along reaction pathways. Focusing on hydrogen abstraction reactions of CN radical with isopentane, we compare the performance of NNs trained using iMD-VR data versus NNs trained using a more traditional method, namely, molecular dynamics (MD) constrained to sample a predefined grid of points along the hydrogen abstraction reaction coordinate. Both the NN trained using iMD-VR data and the NN trained using the constrained MD data reproduce important qualitative features of the reactive PESs, such as a low and early barrier to abstraction. Quantitative analysis shows that NN learning is sensitive to the data set used for training. Our results show that user-sampled structures obtained with the quantum chemical iMD-VR machinery enable excellent sampling in the vicinity of the minimum energy path (MEP). As a result, the NN trained on the iMD-VR data does very well predicting energies that are close to the MEP but less well predicting energies for "off-path" structures. The NN trained on the constrained MD data does better predicting high-energy off-path structures, given that it included a number of such structures in its training set. Computer science ; Virtual reality ; Workflow ; Bottleneck ; Artificial neural network ; Artificial intelligence ; Abstraction ; Virtual screening ; Machine learning ; Molecular dynamics ; Chemistry ; Computational chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies