jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cameron Alexander Hurd Price | Laura Pastor‐Pérez | Tomás Ramı́rez Reina | Jian Liu
Source: Reaction Chemistry & Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Robust mesoporous bimetallic yolk–shell catalysts for chemical CO<sub>2</sub> upgrading <i>via</i> dry reforming of methane A new generation highly efficient and stable mesoporous ZnO/Ni@silica yolk–shell catalyst is designed for chemical CO<sub>2</sub> recycling, to solve the coking and sintering issues of traditional catalysts. Bimetallic strip ; Mesoporous material ; Methane ; Sintering ; Catalysis ; Carbon dioxide reforming ; Materials science ; Chemical engineering ; Mesoporous silica ; Shell (structure) ; Syngas ; Chemistry ; Metallurgy ; Organic chemistry ; Composite material ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Wenzhong Zhang | Sami Hietala | Leonid Khriachtchev | Timo Hatanpää | Bhairavi Doshi | Risto Koivula
Source: ACS Applied Materials & Interfaces (article, 2018-06-12)
Field: Chemistry — Inorganic Chemistry

Intralanthanide Separation on Layered Titanium(IV) Organophosphate Materials via a Selective Transmetalation Process The lanthanides (Ln) are an essential part of many advanced technologies. Our societal transformation toward renewable energy drives their ever-growing demand. The similar chemical properties of the Ln pose fundamental difficulties in separating them from each other, yet high purity elements are crucial for specific applications. Here, we propose an intralanthanide separation method utilizing a group of titanium(IV) butyl phosphate coordination polymers as solid-phase extractants. These materials are characterized, and they contain layered structures directed by the hydrophobic interaction of the alkyl chains. The selective Ln uptake results from the transmetalation reaction (framework metal cation exchange), where the titanium(IV) serves as sacrificial coordination centers. The "tetrad effect" is observed from a dilute Ln<sup>3+</sup> mixture. However, smaller Ln<sup>3+</sup> ions are preferentially extracted in competitive binary separation models between adjacent Ln pairs. The intralanthanide ion-exchange selectivity arises synergistically from the coordination and steric strain preferences, both of which follow the reversed Ln contraction order. A one-step aqueous separation of neodymium (Nd) and dysprosium (Dy) is quantitatively achievable by simply controlling the solution pH in a batch mode, translating into a separation factor of greater than 2000 and 99.1% molar purity of Dy in the solid phase. Coordination polymers provide a versatile platform for further exploring selective Ln separation processes via the transmetalation process. Transmetalation ; Materials science ; Lanthanide ; Ion exchange ; Separation process ; Titanium ; Chemical engineering ; Ion ; Organic chemistry ; Catalysis ; Chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Lanthanide and Transition Metal Complexes
Authors: Tímea R. Kégl | Noémi Pálinkás | László Kollár | Tamás Kégl
Source: Molecules (article, 2018-12-01)
Field: Chemistry — Inorganic Chemistry

Computational Characterization of Bidentate P-Donor Ligands: Direct Comparison to Tolman’s Electronic Parameters The applicability of two types of transition-metal carbonyl complexes as appropriate candidates for computationally derived Tolman's ligand electronic parameters were examined with density functional theory (DFT) calculations employing the B97D3 functional. Both Pd<sup>(0)</sup>L2(CO) and HRh<sup>(I)</sup>L2(CO) complexes correlated well with the experimental Tolman Electronic Parameter scale. For direct comparison of the electronic effects of diphosphines with those of monophosphines, the palladium-containing system is recommended. The t r a n s influence of various phosphines did not show a major difference, but the decrease of the H-Rh-P angle from linear can cause a significant change. Characterization (materials science) ; Denticity ; Chemistry ; Computational chemistry ; Crystallography ; Materials science ; Nanotechnology Asymmetric Hydrogenation and Catalysis ; Organometallic Complex Synthesis and Catalysis ; Metal complexes synthesis and properties
Authors: Dmitry V. Averyanov | Oleg E. Parfenov | Andrey M. Tokmachev | Igor A. Karateev | Oleg A. Kondratev | Alexander N. Taldenkov | М.С. Платунов | F. Wilhelm | Andreï Rogalev | Vyacheslav G. Storchak
Source: Nanotechnology (article, 2018-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fine structure of metal–insulator transition in EuO resolved by doping engineering Metal-insulator transitions (MITs) offer new functionalities for nanoelectronics. However, ongoing attempts to control the resistivity by external stimuli are hindered by strong coupling of spin, charge, orbital and lattice degrees of freedom. This difficulty presents a quest for materials which exhibit MIT caused by a single degree of freedom. In the archetypal ferromagnetic semiconductor EuO, magnetic orders dominate the MIT. Here we report a new approach to take doping under control in this material on the nanoscale: formation of oxygen vacancies is strongly suppressed to exhibit the highest MIT resistivity jump and magnetoresistance among thin films. The nature of the MIT is revealed in Gd doped films. The critical doping is determined to be more than an order of magnitude lower than in all previous studies. In lightly doped films, a remarkable thermal hysteresis in resistivity is discovered. It extends over 100 K in the paramagnetic phase reaching 3 orders of magnitude. In the warming mode, the MIT is shown to be a two-step process. The resistivity patterns are consistent with an active role of magnetic polarons-formation of a narrow band and its thermal destruction. High-temperature magnetic polaron effects include large negative magnetoresistance and ferromagnetic droplets revealed by x-ray magnetic circular dichroism. Our findings have wide-range implications for the understanding of strongly correlated oxides and establish fundamental benchmarks to guide theoretical models of the MIT. Condensed matter physics ; Materials science ; Polaron ; Magnetoresistance ; Ferromagnetism ; Doping ; Metal–insulator transition ; Electrical resistivity and conductivity ; Colossal magnetoresistance ; Paramagnetism ; Magnetism ; Magnetic circular dichroism ; Metal ; Electron ; Magnetic field ; Optoelectronics ; Physics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Jurgen Schill | Sam van Dun | Maarten J. Pouderoijen | Henk M. Janssen | L.‐G. Milroy | Albertus P. H. J. Schenning | Luc Brunsveld
Source: Chemistry - A European Journal (article, 2018-03-23)
Field: Materials Science — Materials Chemistry

Synthesis and Self‐Assembly of Bay‐Substituted Perylene Diimide Gemini‐Type Surfactants as Off‐On Fluorescent Probes for Lipid Bilayers Interest in bay-substituted perylene-3,4:9,10-tetracarboxylic diimides (PDIs) for solution-based applications is growing due to their improved solubility and altered optical and electronic properties compared to unsubstituted PDIs. Synthetic routes to 1,12-bay-substituted PDIs have been very demanding due to issues with steric hindrance and poor regioselectivity. Here we report a simple one-step regioselective and high yielding synthesis of a 1,12-dihydroxylated PDI derivative that can subsequently be alkylated in a straightforward fashion to produce nonplanar 1,12-dialkoxy PDIs. These PDIs show a large Stokes shift, which is specifically useful for bioimaging applications. A particular cationic PDI gemini-type surfactant has been developed that forms nonfluorescent self-assembled particles in water ("off state"), which exerts a high fluorescence upon incorporation into lipophilic bilayers ("on state"). Therefore, this probe is appealing as a highly sensitive fluorescent labelling marker with a low background signal for imaging artificial and cellular membranes. Perylene ; Diimide ; Fluorescence ; Regioselectivity ; Chemistry ; Cationic polymerization ; Membrane ; Combinatorial chemistry ; Photochemistry ; Molecule ; Organic chemistry Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Andreas Kreisel | Brian M. Andersen | P. J. Hirschfeld
Source: Physical review. B./Physical review. B (article, 2018-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Itinerant approach to magnetic neutron scattering of FeSe: Effect of orbital selectivity Recent STM experiments and theoretical considerations have highlighted the role of interaction-driven orbital selectivity in FeSe, and its role in generating the extremely anisotropic superconducting gap structure in this material. We study the magnetic excitation spectrum resulting from the coherent quasiparticles within the same renormalized random phase approximation approach used to explain the STM experiments, and show that it agrees well with the low-energy momentum and energy dependent response measured by inelastic neutron scattering experiments. We find a correlation-induced suppression of $(\ensuremath{\pi},\ensuremath{\pi})$ scattering due to a small quasiparticle weight of states of ${d}_{xy}$ character. We compare predictions for twinned and untwinned crystals, and predict in particular a strongly $(\ensuremath{\pi},0)$ dominated response at low energies in untwinned systems, in contrast to previous itinerant theories. Neutron scattering ; Condensed matter physics ; Scattering ; Selectivity ; Neutron ; Physics ; Nuclear physics ; Chemistry ; Optics Iron-based superconductors research ; Rare-earth and actinide compounds ; Advanced Condensed Matter Physics
Authors: Haitao Chen | Mingkai Liu | Lei Xu | Dragomir N. Neshev
Source: Beilstein Journal of Nanotechnology (article, 2018-03-02)
Field: Materials Science — Materials Chemistry

Valley-selective directional emission from a transition-metal dichalcogenide monolayer mediated by a plasmonic nanoantenna <b>Background:</b> Two-dimensional (2D) transition-metal dichalcogenides (TMDCs) with intrinsically crystal inversion-symmetry breaking have shown many advanced optical properties. In particular, the valley polarization in 2D TMDCs that can be addressed optically has inspired new physical phenomena and great potential applications in valleytronics. <b>Results:</b> Here, we propose a TMDC-nanoantenna system that could effectively enhance and direct emission from the two valleys in TMDCs into diametrically opposite directions. By mimicking the emission from each valley of the monolayer of WSe<sub>2</sub> as a chiral point-dipole emitter, we demonstrate numerically that the emission from different valleys is directed into opposite directions when coupling to a double-bar plasmonic nanoantenna. The directionality derives from the interference between the dipole and quadrupole modes excited in the two bars, respectively. Thus, we could tune the emission direction from the proposed TMDC-nanoantenna system by tuning the pumping without changing the antenna structure. Furthermore, we discuss the general principles and the opportunities to improve the average performance of the nanoantenna structure. <b>Conclusion:</b> The scheme we propose here can potentially serve as an important component for valley-based applications, such as non-volatile information storage and processing. Valleytronics ; Plasmon ; Monolayer ; Dipole ; Materials science ; Polarization (electrochemistry) ; Optoelectronics ; Excited state ; Light emission ; Transition metal ; Quadrupole ; Point reflection ; Nanotechnology ; Physics ; Condensed matter physics ; Chemistry ; Spintronics 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Hang Zhang | Kedar Hippalgaonkar | Tonio Buonassisi | Ole Martin Løvvik | Espen Sagvolden | Ding Ding
Source: ES Energy & Environments (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges High-throughput computational and experimental design of materials has becoming an increasingly important field in material science overall. However, this area of research has largely been only been emerging in thermal sciences, in part due to the computational and experimental challenges faced in obtaining thermal properties of materials. In this paper, we provide a current overview of some of the recent work and highlight the challenges and opportunities that are ahead of us in this field. In particular, we focus on the use of machine learning and highthroughput methods for screening of thermal conductivity for compounds, composite and alloys and interface conductance. These methods have brought about a feedback mechanism for understanding new correlations and identifying new descriptors, speeding up the discovery of novel thermal materials. Data science ; Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal and Kinetic Analysis
Authors: Stefano Piersanti | Antonio Orlandi | Francesco de Paulis
Source: IEEE Transactions on Electromagnetic Compatibility (article, 2018-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Absorbing Materials Design by Optimization Using a Machine Learning Approach One of the state-of-the-art strategies to decrease the isolation between sections of personal digital assistance devices, taking into account the unavoidable thermal, mechanical, weight, and manufacturability constraints is the proper introduction of electromagnetic (EM) absorbing materials. The global target of this study is the optimized synthesis of the EM properties of a material using a machine learning approach. The sought material must improve the shielding effectiveness between two regions of a device. The first part of this paper is dedicated to the assessment of the performances of an artificial neural network used as function approximation to compute the values of the shielding without resorting to complex and time-consuming full-wave numerical simulations. The network is assessed with respect to either the variations of the training set and algorithm or of its architecture. Significant figures of merit are introduced. The second part is devoted to the description and definition of nature-inspired algorithms demanded to manage the optimization problem. Design for manufacturability ; Electromagnetic shielding ; Computer science ; Artificial neural network ; Function (biology) ; Set (abstract data type) ; Computer engineering ; Electronic engineering ; Mathematical optimization ; Mechanical engineering ; Artificial intelligence ; Engineering ; Mathematics ; Electrical engineering Electromagnetic wave absorption materials ; RFID technology advancements ; Electromagnetic Compatibility and Measurements
Authors: Aditya Nandy | Chenru Duan | Jon Paul Janet | Stefan Gugler | Heather J. Kulik
Source: ChemRxiv (preprint, 2018-08-21)
Field: Materials Science — Materials Chemistry

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO-LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO-LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20-30 features. Analysis of the essential descriptors for HOMO and HOMO-LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveals the paramount importance of non-local, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO-LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require. HOMO/LUMO ; Test set ; Artificial intelligence ; Machine learning ; Computer science ; Artificial neural network ; Chemical space ; Chemistry ; Molecule ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: N. Castin | M.I. Pascuet | Luca Messina | Christophe Domain | Pär Olsson | R.C. Pasianot | L. Malerba
Source: Computational Materials Science (article, 2018-02-23)
Field: Materials Science — Materials Chemistry

Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks Artificial neural network ; Computer science ; Bridging (networking) ; Multiscale modeling ; Parametrization (atmospheric modeling) ; Atomic units ; Scale (ratio) ; Artificial intelligence ; Physics ; Chemistry Nuclear Materials and Properties ; Fusion materials and technologies ; Advanced Materials Characterization Techniques
Authors: Matías Núñez
Source: Computational Materials Science (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

Exploring materials band structure space with unsupervised machine learning Embedding ; Computer science ; Space (punctuation) ; Brillouin zone ; Feature (linguistics) ; Unsupervised learning ; Cluster (spacecraft) ; Visualization ; Feature vector ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Physics ; Optics Machine Learning in Materials Science ; Neural Networks and Applications ; Software Engineering Research
Authors: Pascal Friederich | Manuel Konrad | Timo Strunk | Wolfgang Wenzel
Source: Scientific Reports (article, 2018-02-01)
Field: Materials Science — Materials Chemistry

Machine learning of correlated dihedral potentials for atomistic molecular force fields Computer simulation increasingly complements experimental efforts to describe nanoscale structure formation. Molecular mechanics simulations and related computational methods fundamentally rely on the accuracy of classical atomistic force fields for the evaluation of inter- and intramolecular energies. One indispensable component of such force fields, in particular for large organic molecules, is the accuracy of molecule-specific dihedral potentials which are the key determinants of molecular flexibility. We show in this work that non-local correlations of dihedral potentials play a decisive role in the description of the total molecular energy-an effect which is neglected in most state-of-the-art dihedral force fields. We furthermore present an efficient machine learning approach to compute intramolecular conformational energies. We demonstrate with the example of α-NPD, a molecule frequently used in organic electronics, that this approach outperforms traditional force fields by decreasing the mean absolute deviations by one order of magnitude to values smaller than 0.37 kcal/mol (16.0 meV) per dihedral angle. Dihedral angle ; Intramolecular force ; Force field (fiction) ; Molecular dynamics ; Molecule ; Flexibility (engineering) ; Work (physics) ; Computer science ; Statistical physics ; Chemical physics ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xiayue Li | Farren S. Curtis | Timothy Rose | Christoph Schober | Alvaro Vazquez-Mayagoitia | Karsten Reuter | Harald Oberhofer | Noa Marom
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases. Workflow ; Python (programming language) ; Crystal structure prediction ; Superposition principle ; Cluster analysis ; Density functional theory ; Set (abstract data type) ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jiangbo Shi | Balázs Fekete | Jihui Wang | Digby D. Macdonald
Source: Corrosion Science (article, 2018-04-30)
Field: Materials Science — Metals and Alloys

Customization of the coupled environment fracture model for predicting stress corrosion cracking in Alloy 600 in PWR environment Stress corrosion cracking ; Materials science ; Alloy ; Intergranular corrosion ; Coolant ; Metallurgy ; Corrosion ; Cracking ; Environmental stress fracture ; Stress intensity factor ; Hydrogen embrittlement ; Stress (linguistics) ; Fracture (geology) ; Fracture mechanics ; Composite material ; Engineering ; Mechanical engineering Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Non-Destructive Testing Techniques