jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Si-Nae Park | Se‐Yun Kim | Sang-Ju Lee | Shi‐Joon Sung | Kee‐Jeong Yang | Jin‐Kyu Kang | Dae‐Hwan Kim
Source: Materials Advances (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Facile growth of a Sb<sub>2</sub>Se<sub>3</sub> nanorod array induced by a MoSe<sub>2</sub> interlayer and its application in 3D p–n junction solar cells A Sb 2 Se 3 nanorod array was uniformly grown by co-evaporation on a MoSe 2 interlayer. MoSe 2 interlayer improves the preferential growth and contact quality of Sb 2 Se 3 nanorods. Nanorod ; Materials science ; Evaporation ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Thermodynamics ; Organic chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Takashi Uchihashi
Source: AAPPS bulletin (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Surface atomic-layer superconductors with Rashba/Zeeman-type spin-orbit coupling Abstract In this article, we review the recent progress in surface atomic-layer superconductors on semiconductor substrates with Rashba/Zeeman-type spin-orbit coupling (SOC). After introduction of some of the basics of Rashba/Zeeman-type SOC and its effects on superconductivity, representative surface structures with relevant features are described in terms of their crystalline and electronic properties. This is followed by recent experimental studies that have revealed anomalous superconducting phenomena, which can be attributed to the effects of Rashba/Zeeman-type SOC. Future prospects, likely to be driven by instrumentational developments, are given as a concluding remark. Zeeman effect ; Condensed matter physics ; Superconductivity ; Spin–orbit interaction ; Coupling (piping) ; Semiconductor ; Rashba effect ; Physics ; Spin (aerodynamics) ; Surface (topology) ; Type (biology) ; Materials science ; Quantum mechanics ; Spintronics ; Magnetic field Electronic and Structural Properties of Oxides ; Quantum and electron transport phenomena ; Surface and Thin Film Phenomena
Authors: Stephan Roche | Stephen R. Power | Branislav K. Nikolić | José H. García | Antti‐Pekka Jauho
Source: Journal of Physics Materials (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Have mysterious topological valley currents been observed in graphene superlattices? Abstract We provide a critical discussion concerning the claim of topological valley currents, driven by a global Berry curvature and valley Hall effect proposed in recent literature. After pointing out a major inconsistency of the theoretical scenario proposed to interpret giant nonlocal resistance, we discuss various possible alternative explanations and open directions of research to solve the mystery of nonlocal transport in graphene superlattices. Superlattice ; Graphene ; Condensed matter physics ; Physics ; Topology (electrical circuits) ; Theoretical physics ; Quantum mechanics ; Engineering ; Electrical engineering Graphene research and applications ; Advanced Memory and Neural Computing ; Topological Materials and Phenomena
Authors: Tapajyoti Das Gupta | Louis Martin‐Monier | Jérémy Butet | Kuang-Yu Yang | Andreas Leber | Chaoqun Dong | Tùng Nguyen-Dang | Wei Yan | Olivier J. F. Martin | Fabien Sorin
Source: Nanophotonics (article, 2021-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Second harmonic generation in glass‐based metasurfaces using tailored surface lattice resonances Abstract Dielectric metasurfaces have shown prominent applications in nonlinear optics due to strong field enhancement and low dissipation losses at the nanoscale. Chalcogenide glasses are one of the promising materials for the observation of nonlinear effects thanks to their high intrinsic nonlinearities. Here, we demonstrate, experimentally and theoretically, that significant second harmonic generation (SHG) can be obtained within amorphous Selenium (Se)‐based chalcogenide metasurfaces by exploiting the coupling between lattice and particle resonances. We further show that the high‐quality factor resonance at the origin of the SHG can be tuned over a wide wavelength range using a simple and versatile fabrication approach. The measured second harmonic intensity is orders of magnitude higher than that from a dewetted Se film consisting of random Se nanoparticles. The achieved conversion efficiency in the resonance region is of the order of 10 −6 which is comparable with direct bandgap materials and at least two orders of magnitude higher than that of conventional plasmonics‐ and Si‐based structures. Fabricated via a simple and scalable technique, these all‐dielectric architectures are ideal candidates for the design of flat nonlinear optical components on flexible substrates. Chalcogenide ; Materials science ; Dielectric ; Optoelectronics ; Second-harmonic generation ; Plasmon ; Fabrication ; Chalcogenide glass ; Optics ; Nonlinear optics ; Wavelength ; Femtosecond ; Laser ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Kyungjun Choi | Seong Jun Park | Mooncheol Won | Cheol Hoon Park
Source: Sensors (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Soft Inductive Coil Spring Strain Sensor Integrated with SMA Spring Bundle Actuator This study proposes a soft inductive coil spring (SICS) strain sensor that can measure the strain of soft actuators. The SICS sensor, produced by transforming a shape memory alloy (SMA) wire with the same materials as that of an SMA spring bundle actuator (SSBA) into a coil spring shape, measures inductance changes according to length changes. This study also proposes a manufacturing method, output characteristics of the SICS sensor applicable to the SSBA among soft actuators, and the structure of the SICS sensor-integrated SSBA (SI-SSBA). In the SI-SSBA, the SMA spring bundle and SICS sensor have structures corresponding to the muscle fiber and spindle of the skeletal muscle, respectively. It is demonstrated that when a robotic arm with one degree of freedom is operated by attaching two SI-SSBAs in an antagonistic structure, the displacement of the SSBA can be measured using the proposed strain sensor. The output characteristics of the SICS sensor for the driving speed of the robotic arm were evaluated, and it was experimentally proven that the strain of the SSBA can be stably measured in water under a temperature change of 54 °C from 36 to 90 °C. Coil spring ; SMA* ; Actuator ; Spring (device) ; Shape-memory alloy ; Displacement (psychology) ; Inductive sensor ; Bundle ; Electromagnetic coil ; Materials science ; Acoustics ; Structural engineering ; Computer science ; Engineering ; Composite material ; Electrical engineering ; Physics Shape Memory Alloy Transformations ; Advanced Sensor and Energy Harvesting Materials ; Soft Robotics and Applications
Authors: Ziyu Xiang | Mingzhou Fan | Guillermo Vázquez Tovar | William Trehern | Byung-Jun Yoon | Xiaofeng Qian | Raymundo Arróyave | Xiaoning Qian
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. We focus on materials science applications, where interpretable predictive modeling may provide principled understanding of materials systems and guide new materials discovery. It is often computationally prohibitive to exhaust all the potential relationships to construct and search the whole feature space to identify interpretable and predictive features. We develop and evaluate new AFE strategies by exploring a feature generation tree (FGT) with deep Q-network (DQN) for scalable and efficient exploration policies. The developed DQN-based AFE strategies are benchmarked with the existing AFE methods on several materials science datasets. Feature (linguistics) ; Feature engineering ; Construct (python library) ; Computer science ; Artificial intelligence ; Scalability ; Machine learning ; Dependency (UML) ; Tree (set theory) ; Focus (optics) ; Feature vector ; Deep learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andrés Osorio Salazar | Yusuke Sugahara | Daisuke Matsuura | Yukio Takeda
Source: Machines (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

Scalable Output Linear Actuators, a Novel Design Concept Using Shape Memory Alloy Wires Driven by Fluid Temperature In this paper, the concept of scalability for actuators is introduced and explored, which is the capability to freely change the output characteristics on demand: displacement and force for a linear actuator, angular position and torque for a rotational actuator. This change can either be used to obtain power improvement (with a constant scale factor), or to improve the usability of a robotic system according to variable conditions (with a variable scale factor). Some advantages of a scalable design include the ability to adapt to changing environments, variable resolution of step size, ability to produce designs that are adequate for restricted spaces or that require strict energy efficiency, and intrinsically safe systems. Current approaches for scalability in actuators have shortcomings: the method to achieve scalability is complex, so obtaining a variable scaling factor is challenging, or they cannot scale both output characteristics simultaneously. Shape Memory Alloy (SMA) wire-based actuators can overcome these limitations, because its two output characteristics, displacement and force, are physically independent from each other. In this paper we present a novel design concept for linear scalable actuators that overcome SMA design and scalability limitations by using a variable number of SMA wires mechanically in parallel, immersed in a liquid that transmits heat from a separate heat source (wet activation). In this concept, more wires increase the maximum attainable force, and longer wires increase the maximum displacement. Prototypes with different number of SMA wires were constructed and tested in isometric experiments to determine force vs. temperature behavior and time response. The heat-transmitting liquid was either static or flowing using pumps. Scalability was achieved with a simple method in all tested prototypes with a linear correlation of maximum force to number of SMA wires. Flowing heat transmission achieved higher actuation bandwidth. Actuator ; Scalability ; SMA* ; Shape-memory alloy ; Scale factor (cosmology) ; Torque ; Control theory (sociology) ; Computer science ; Displacement (psychology) ; Mechanical engineering ; Materials science ; Engineering ; Physics ; Control (management) ; Artificial intelligence Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Innovations in Concrete and Construction Materials
Authors: Sergiu Coseri
Source: Polymers (review, 2021-02-25)
Field: Materials Science — Biomaterials

Insights on Cellulose Research in the Last Two Decades in Romania In the current global context, cellulose fulfills those characteristics that give it clear advantages over synthetic fibers, having a huge potential for substituting fossil-based materials which are polluting and harmful to ecosystems. Research conducted in most laboratories around the world in the field of cellulose is overwhelmingly aimed at industrial needs because features such as renewability and low cost are the most important attributes for economic success. In this global effort, Romanian researchers contribute through achievements that are briefly reviewed in this paper. These refer to the main achievements reported after 2000 in the field of cellulose characterization and cellulose functionalization, as well as the main areas where cellulose-based materials were applied. Cellulose ; Context (archaeology) ; Romanian ; Surface modification ; Engineering ; Environmental science ; Chemical engineering ; Geography Advanced Cellulose Research Studies ; Material Properties and Processing ; Aerogels and thermal insulation
Authors: K. Walczak | Wojciech Sikorski
Source: Energies (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System The article presents an innovative system for non-contact high voltage (HV) measurement, which extends the measurement capabilities of a portable partial discharges (PD) monitoring system intended for diagnostics of power transformers. The proposed method and the developed measuring system are based on the use of a capacitive probe, thanks to which the high voltage measurement is safe (galvanic separation from the objects at ahigh potential). It is also flexible because the voltage ratio of this system can be configured in a wide range by changing the probe’s position. The proposed solution makes the portable PD monitoring system fully autonomous and independent of the substation systems and devices. The article presents both the concept of the non-contact HV measurement system and its practical implementation. The procedure for determining the voltage ratio and measurement uncertainty, which is at an acceptable level of 1–5% in laboratory conditions, was discussed in detail. In addition, the article discusses the digital filtering and wavelet de-noising methods implemented in the software of the monitoring system, which makes it possible to measure the voltage in the presence of strong electromagnetic disturbances occurring at the substation. Finally, the results of field tests carried out on a 250 MVA power transformer are presented, which confirmed the high accuracy of the HV measurement using a capacitive probe and the advantages of this technique. System of measurement ; Voltage ; Capacitive sensing ; Partial discharge ; High voltage ; Electrical engineering ; Electronic engineering ; Transformer ; Engineering ; Electric power system ; Software ; Computer science ; Power (physics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Mariia V. Ivonina | Yuuichi Orimoto | Yuriko Aoki
Source: The Journal of Chemical Physics (article, 2021-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum chemistry–machine learning approach for predicting and elucidating molecular hyperpolarizability: Application to [2.2]paracyclophane-containing push–pull polymers Nonlinear optical properties of organic chromophores are of great interest in diverse photonic and optoelectronic applications. To elucidate general trends in the behaviors of molecules, large amounts of data are required. Therefore, both an accurate and a rapid computational approach can significantly promote the theoretical design of molecules. In this work, we combined quantum chemistry and machine learning (ML) to study the first hyperpolarizability (β) in [2.2]paracyclophane-containing push-pull compounds with various terminal donor/acceptor pairs and molecular lengths. To generate reference β values for ML, the ab initio elongation finite-field method was used, allowing us to treat long polymer chains with linear scale efficiency and high computational accuracy. A neural network (NN) model was built for β prediction, and the relevant molecular descriptors were selected using a genetic algorithm. The established NN model accurately reproduced the β values (R<sup>2</sup> > 0.99) of long molecules based on the input quantum chemical properties (dipole moment, frontier molecular orbitals, etc.) of only the shortest systems and additional information about the actual system length. To obtain general trends in molecular descriptor-target property relationships learned by the NN, three approaches for explaining the ML decisions (i.e., partial dependence, accumulated local effects, and permutation feature importance) were used. The effect of donor/acceptor alternation on β in the studied systems was examined. The asymmetric extension of molecular regions end-capped with donors and acceptors produced unequal β responses. The results revealed how the electronic properties originating from the nature of substituents on the microscale controlled the magnitude of β according to the NN approximation. The applied approach facilitates the conceptual discoveries in chemistry by using ML to both (i) efficiently generate data and (ii) provide a source of information about causal correlations among system properties. Hyperpolarizability ; Chromophore ; Molecule ; Ab initio ; Computational chemistry ; Quantum chemistry ; Dipole ; Acceptor ; Chemical physics ; Molecular orbital ; Quantum ; Chemistry ; Computer science ; Materials science ; Polarizability ; Physics ; Quantum mechanics ; Organic chemistry Nonlinear Optical Materials Research ; Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry
Authors: Nick Gerrits
Source: The Journal of Physical Chemistry Letters (article, 2021-12-17)
Field: Materials Science — Materials Chemistry

Accurate Simulations of the Reaction of H<sub>2</sub>on a Curved Pt Crystal through Machine Learning Theoretical studies on molecule-metal surface reactions have so far been limited to small surface unit cells due to computational costs. Here, for the first time molecular dynamics simulations on very large surface unit cells at the level of density functional theory are performed, allowing a direct comparison to experiments performed on a curved crystal. Specifically, the reaction of D<sub>2</sub> on a curved Pt crystal is investigated with a neural network potential (NNP). The developed NNP is also accurate for surface unit cells considerably larger than those that have been included in the training data, allowing dynamical simulations on very large surface unit cells that otherwise would have been intractable. Important and complex aspects of the reaction mechanism are discovered such as diffusion and a shadow effect of the step. Furthermore, conclusions from simulations on smaller surface unit cells cannot always be transfered to larger surface unit cells, limiting the applicability of theoretical studies of smaller surface unit cells to heterogeneous catalysts with small defect densities. Unit (ring theory) ; Crystal (programming language) ; Limiting ; Surface (topology) ; Chemical physics ; Molecular dynamics ; Materials science ; Diffusion ; Shadow (psychology) ; Density functional theory ; Chemistry ; Nanotechnology ; Computational chemistry ; Physics ; Computer science ; Thermodynamics ; Geometry ; Mathematics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Jiarui Yang | Wenhao Li | Dingsheng Wang
Source: Chem Catalysis (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Machine learning: The trends of developing high-efficiency single-atom materials In this issue of Chem Catalysis, Singh and co-workers apply machine learning to screen high-efficiency single-atom materials in the nitrogen reduction reaction. On the basis of this model, the authors are able to predict reliable reaction results without conducting an experiment, showing promise for the future of the field of heterogenous catalysis. Nitrogen atom ; Catalysis ; Atom (system on chip) ; Atom economy ; Field (mathematics) ; Reduction (mathematics) ; Reaction conditions ; Nanotechnology ; Chemistry ; Computer science ; Biochemical engineering ; Materials science ; Process engineering ; Combinatorial chemistry ; Engineering ; Mathematics ; Organic chemistry ; Parallel computing Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shreshth A. Malik | Rhys E. A. Goodall | Alpha A. Lee
Source: Chemistry of Materials (article, 2021-01-05)
Field: Materials Science — Materials Chemistry

Predicting the Outcomes of Material Syntheses with Deep Learning A key bottleneck for material discovery is synthesis. While significant advances have been made in computational material design, synthesis pathways are still often determined through trial and error. In this work, we develop a method that predicts the major product of solid-state reactions. The main advance presented here is the construction of fixed-length, learned representations of reactions. Precursors are represented as nodes on a “reaction graph”, and message-passing operations between nodes are used to embody the interactions between precursors in the reaction mixture. We show that this deep learning framework not only outperforms baseline methods but also more reliably assesses the uncertainty in its predictions. Moreover, our approach establishes a quantitative metric for inorganic reaction similarity, allowing the user to explain model predictions and retrieve relevant literature sources. Bottleneck ; Computer science ; Baseline (sea) ; Graph ; Feature (linguistics) ; Theoretical computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ehsan Alibagheri | Bohayra Mortazavi | Timon Rabczuk
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Electronic and Structural Properties of Two-Dimensional Materials Using Machine Learning Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features. In this work, we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy. Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties. Computer science ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Material properties ; Materials science Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications