jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: T. P. Lyons | D. Gillard | A. Molina-Sánchez | A. Misra | F. Withers | P. S. Keatley | A. Kozikov | T. Taniguchi | K. Watanabe | K. S. Novoselov | J. Fernández-Rossier | A. I. Tartakovskii
Source: Nature Communications (article, 2020-11-26)
Field: Materials Science — Materials Chemistry

Interplay between spin proximity effect and charge-dependent exciton dynamics in MoSe2/CrBr3 van der Waals heterostructures Semiconducting ferromagnet-nonmagnet interfaces in van der Waals heterostructures present a unique opportunity to investigate magnetic proximity interactions dependent upon a multitude of phenomena including valley and layer pseudospins, moiré periodicity, or exceptionally strong Coulomb binding. Here, we report a charge-state dependency of the magnetic proximity effects between MoSe<sub>2</sub> and CrBr<sub>3</sub> in photoluminescence, whereby the valley polarization of the MoSe<sub>2</sub> trion state conforms closely to the local CrBr<sub>3</sub> magnetization, while the neutral exciton state remains insensitive to the ferromagnet. We attribute this to spin-dependent interlayer charge transfer occurring on timescales between the exciton and trion radiative lifetimes. Going further, we uncover by both the magneto-optical Kerr effect and photoluminescence a domain-like spatial topography of contrasting valley polarization, which we infer to be labyrinthine or otherwise highly intricate, with features smaller than 400 nm corresponding to our optical resolution. Our findings offer a unique insight into the interplay between short-lived valley excitons and spin-dependent interlayer tunneling, while also highlighting MoSe<sub>2</sub> as a promising candidate to optically interface with exotic spin textures in van der Waals structures. Trion ; Exciton ; van der Waals force ; Heterojunction ; Coulomb ; Photoluminescence ; Polarization (electrochemistry) ; Spin (aerodynamics) 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Iron-based superconductors research
Authors: Ryan Murdock | Steven K. Kauwe | Anthony Wang | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2020-08-27)
Field: Materials Science — Materials Chemistry

Is Domain Knowledge Necessary for Machine Learning Materials Properties? Computer science ; Materials informatics ; Field (mathematics) ; Domain (mathematical analysis) ; Machine learning ; Domain knowledge ; Simple (philosophy) ; Artificial intelligence ; Noise (video) ; Data mining ; Data science ; Health informatics ; Mathematics ; Engineering informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Marm Dixit | Nikhilendra Singh | James P. Horwath | Pavel Shevchenko | Michael D. Jones | Eric A. Stach | Timothy S. Arthur | Kelsey B. Hatzell
Source: Matter (article, 2020-10-12)
Field: Materials Science — Materials Chemistry

In Situ Investigation of Chemomechanical Effects in Thiophosphate Solid Electrolytes Thiophosphate ; In situ ; Electrolyte ; Chemistry ; Materials science ; Physical chemistry ; Electrode ; Organic chemistry Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Solid-state spectroscopy and crystallography
Authors: Sebastian Spicher | Stefan Grimme
Source: Angewandte Chemie (article, 2020-04-28)
Field: Chemistry — Physical and Theoretical Chemistry

Robust Atomistic Modeling of Materials, Organometallic, and Biochemical Systems Abstract Modern chemistry seems to be unlimited in molecular size and elemental composition. Metal‐organic frameworks or biological macromolecules involve complex architectures and a large variety of elements. Yet, a general and broadly applicable theoretical method to describe the structures and interactions of molecules beyond the 1000‐atom size regime semi‐quantitatively is not self‐evident. For this purpose, a generic force field named GFN‐FF is presented, which is completely newly developed to enable fast structure optimizations and molecular‐dynamics simulations for basically any chemical structure consisting of elements up to radon. The freely available computer program requires only starting coordinates and elemental composition as input from which, fully automatically, all potential‐energy terms are constructed. GFN‐FF outperforms other force fields in terms of generality and accuracy, approaching the performance of much more elaborate quantum‐mechanical methods in many cases. Molecular dynamics ; Force field (fiction) ; Field (mathematics) ; Nanotechnology ; Generality ; Quantum ; Chemical physics ; Molecule ; Computer science ; Statistical physics ; Biological system ; Chemistry ; Computational chemistry ; Biochemical engineering ; Materials science ; Physics ; Mathematics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Eric Ashalley | Kingsley Nketia Acheampong | Lucas V. Besteiro | Peng Yu | Arup Neogi | Alexander O. Govorov | Zhiming M. Wang
Source: Photonics Research (article, 2020-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multitask deep-learning-based design of chiral plasmonic metamaterials The field of chiral plasmonics has registered considerable progress with machine-learning (ML)-mediated metamaterial prototyping, drawing from the success of ML frameworks in other applications such as pattern and image recognition. Here, we present an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. Our model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Finally, we explore the behavior of a sample ML-optimized structure in a practical application, assisting the sensing of biomolecular enantiomers. Other potential applications of our metastructure include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy, with our ML framework being applicable to a wider range of physical problems. Metamaterial ; Computer science ; Plasmon ; Artificial intelligence ; Deep learning ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior
Authors: Andriy R. Kuzmyn | Ai T. Nguyen | Lucas W. Teunissen | Han Zuilhof | Jacob Baggerman
Source: Langmuir (article, 2020-04-15)
Field: Materials Science — Surfaces, Coatings and Films

Antifouling Polymer Brushes via Oxygen-Tolerant Surface-Initiated PET-RAFT This work presents a new method for the synthesis of antifouling polymer brushes using surface-initiated photoinduced electron transfer-reversible addition-fragmentation chain-transfer polymerization with eosin Y and triethanolamine as catalysts. This method proceeds in an aqueous environment under atmospheric conditions without any prior degassing and without the use of heavy metal catalysts. The versatility of the method is shown by using three chemically different monomers: oligo(ethylene glycol) methacrylate, <i>N</i>-(2-hydroxypropyl)methacrylamide, and carboxybetaine methacrylamide. In addition, the light-triggered nature of the polymerization allows the creation of complex three-dimensional structures. The composition and topological structuring of the brushes are confirmed by X-ray photoelectron spectroscopy and atomic force microscopy. The kinetics of the polymerizations are followed by measuring the layer thickness with ellipsometry. The polymer brushes demonstrate excellent antifouling properties when exposed to single-protein solutions and complex biological matrices such as diluted bovine serum. This method thus presents a new simple approach for the manufacturing of antifouling coatings for biomedical and biotechnological applications. Methacrylamide ; Polymer ; Chain transfer ; Polymerization ; Chemical engineering ; Polymer chemistry ; Raft ; Ethylene glycol ; Materials science ; Radical polymerization ; Monomer ; Chemistry ; Organic chemistry ; Acrylamide Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Surface Modification and Superhydrophobicity
Authors: Stephanie J. Urwin | Guillaume Levilain | Ivan Marziano | Jeremy M. Merritt | Ian Houson | Joop H. ter Horst
Source: Organic Process Research & Development (review, 2020-07-06)
Field: Materials Science — Materials Chemistry

A Structured Approach To Cope with Impurities during Industrial Crystallization Development The perfect separation with optimal productivity, yield, and purity is very difficult to achieve. Despite its high selectivity, in crystallization unwanted impurities routinely contaminate a crystallization product. Awareness of the mechanism by which the impurity incorporates is key to understanding how to achieve crystals of higher purity. Here, we present a general workflow which can rapidly identify the mechanism of impurity incorporation responsible for poor impurity rejection during a crystallization. A series of four general experiments using standard laboratory instrumentation is required for successful discrimination between incorporation mechanisms. The workflow is demonstrated using four examples of active pharmaceutical ingredients contaminated with structurally related organic impurities. Application of this workflow allows a targeted problem-solving approach to the management of impurities during industrial crystallization development, while also decreasing resources expended on process development. Crystallization ; Impurity ; Workflow ; Process development ; Yield (engineering) ; Process engineering ; Materials science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Computer science ; Metallurgy ; Engineering ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Etienne S. Gauthier | Laura Abella | Nora Hellou | Benoît Darquié | Elsa Caytan | Thierry Roisnel | Nicolas Vanthuyne | Ludovic Favereau | Monika Srebro‐Hooper | J. A. Gareth Williams | Jochen Autschbach | Jeanne Crassous
Source: Angewandte Chemie International Edition (article, 2020-03-13)
Field: Chemistry — Organic Chemistry

Long‐Lived Circularly Polarized Phosphorescence in Helicene‐NHC Rhenium(I) Complexes: The Influence of Helicene, Halogen, and Stereochemistry on Emission Properties The first enantiopure chiral-at-rhenium complexes of the form fac-ReX(CO)<sub>3</sub> (:C^N) have been prepared, where :C^N is a helicene-N-heterocyclic carbene (NHC) ligand and X=Cl or I. These have complexes show strong changes in the emission characteristics, notably strongly enhanced phosphorescence lifetimes (reaching 0.7 ms) and increased circularly polarized emission (CPL) activity, as compared to their parent chiral models lacking the helicene unit. The halogen along with its position within the dissymmetric stereochemical environment strongly affect the photophysics of the complexes, particularly the phosphorescence quantum yield and lifetime. These results give fresh insight into fine tuning of photophysical and chiroptical properties of Re-NHC systems. Helicene ; Phosphorescence ; Enantiopure drug ; Chemistry ; Halogen ; Rhenium ; Photochemistry ; Carbene ; Quantum yield ; Luminescence ; Stereochemistry ; Crystallography ; Fluorescence ; Molecule ; Materials science ; Organic chemistry ; Enantioselective synthesis ; Physics ; Alkyl ; Catalysis ; Optoelectronics N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: César de Julián Fernández | Claudio Sangregorio | Juan de la Figuera | Blaž Belec | Darko Makovec | Adrián Quesada
Source: Journal of Physics D Applied Physics (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Topical Review: Progress and Prospects of Hard Hexaferrites for Permanent Magnet Applications Permanent magnets based on hard hexaferrite represent the largest family of magnets being used today by volume. They generate moderate remanence induction, but present crucial advantages in terms of availability, cost, resistance to demagnetization and corrosion and absence of eddy current losses. As a consequence, ferrites are the most logical candidate for substitution of rare-earths in selected applications that do not demand the best performing magnets. If the remanence of ferrite-based magnets was to be improved, even mildly, the door to a larger scale substitution could be opened. In this framework, we review here current strategies to improve the properties of hexaferrites for permanent magnet applications. We first discuss the potential of exploring the nanoscale. Second, progress related to controllably doping hexaferrites is revised. Third, results achieved by fabricating hard-soft magnetic composites using ferrites as the hard phase are presented. Finally, future prospects and new potential end applications for ferrite magnets are discussed. Magnet ; Remanence ; Materials science ; Demagnetizing field ; Ferrite (magnet) ; Engineering physics ; Nuclear engineering ; Mechanical engineering ; Composite material ; Magnetization ; Magnetic field ; Physics ; Engineering Magnetic Properties and Synthesis of Ferrites ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Changwon Suh | Clyde Fare | James A. Warren | Edward O. Pyzer‐Knapp
Source: Annual Review of Materials Research (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice. Data science ; Computer science ; Field (mathematics) ; Maturity (psychological) ; Scientific discovery ; Artificial intelligence ; Knowledge extraction ; Machine learning ; Nanotechnology ; Materials science ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Christopher Yeung | Ju-Ming Tsai | Brian King | Benjamin Pham | David Ho | Julia Liang | Mark W. Knight | Aaswath P. Raman
Source: arXiv (Cornell University) (article, 2020-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiplexed Supercell Metasurface Design and Optimization with Tandem Residual Networks Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal-insulator-metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure-property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks that is applicable to complex nanophotonic structures composed of multiple subunit elements that may exhibit coupling. Nanophotonics ; Supercell ; Metamaterial ; Computer science ; Tandem ; Multiplexing ; Residual ; Network planning and design ; Absorption (acoustics) ; Broadband ; Materials science ; Optoelectronics ; Physics ; Optics ; Telecommunications ; Algorithm Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Junling He | Yang Yang | Huacheng Zhu | Kang Li | Wei Yao | Kama Huang
Source: Applied Thermal Engineering (article, 2020-06-16)
Field: Chemistry — Organic Chemistry

Microwave heating based on two rotary waveguides to improve efficiency and uniformity by gradient descent method Materials science ; Microwave ; Radiation ; Descent (aeronautics) ; Mechanical engineering ; Microwave heating ; Gradient descent ; Finite element method ; Optics ; Mechanics ; Computer science ; Structural engineering ; Engineering ; Aerospace engineering ; Physics Microwave-Assisted Synthesis and Applications ; Photonic and Optical Devices ; Food Drying and Modeling
Authors: Alexander Corletto | Joseph G. Shapter
Source: Advanced Science (review, 2020-11-23)
Field: Materials Science — Materials Chemistry

Nanoscale Patterning of Carbon Nanotubes: Techniques, Applications, and Future Carbon nanotube (CNT) devices and electronics are achieving maturity and directly competing or surpassing devices that use conventional materials. CNTs have demonstrated ballistic conduction, minimal scaling effects, high current capacity, low power requirements, and excellent optical/photonic properties; making them the ideal candidate for a new material to replace conventional materials in next-generation electronic and photonic systems. CNTs also demonstrate high stability and flexibility, allowing them to be used in flexible, printable, and/or biocompatible electronics. However, a major challenge to fully commercialize these devices is the scalable placement of CNTs into desired micro/nanopatterns and architectures to translate the superior properties of CNTs into macroscale devices. Precise and high throughput patterning becomes increasingly difficult at nanoscale resolution, but it is essential to fully realize the benefits of CNTs. The relatively long, high aspect ratio structures of CNTs must be preserved to maintain their functionalities, consequently making them more difficult to pattern than conventional materials like metals and polymers. This review comprehensively explores the recent development of innovative CNT patterning techniques with nanoscale lateral resolution. Each technique is critically analyzed and applications for the nanoscale-resolution approaches are demonstrated. Promising techniques and the challenges ahead for future devices and applications are discussed. Nanotechnology ; Nanoscopic scale ; Carbon nanotube ; Materials science Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Graphene research and applications
Authors: Tim Van De Looverbosch | Md. Hafizur Rahman Bhuiyan | Pieter Verboven | Manuel Dierick | Denis Van Loo | Jan De Beenhouwer | Jan Sijbers | Bart Nicolaı̈
Source: Food Control (article, 2020-02-16)
Field: Chemistry — Analytical Chemistry

Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning PEAR ; Nondestructive testing ; Quality (philosophy) ; Computer science ; Materials science ; Horticulture ; Artificial intelligence ; Biology ; Medicine ; Physics ; Radiology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Daniel Markl | Martin Warman | Melanie Dumarey | Eva-Lotta Bergman | Staffan Folestad | Zhenqi Shi | Leo Francis Manley | Daniel Goodwin | J. Axel Zeitler
Source: International Journal of Pharmaceutics (review, 2020-04-20)
Field: Chemistry — Analytical Chemistry

Review of real-time release testing of pharmaceutical tablets: State-of-the art, challenges and future perspective Workflow ; Quality (philosophy) ; Computer science ; Process (computing) ; Product (mathematics) ; Process management ; Key (lock) ; Risk analysis (engineering) ; Engineering ; Systems engineering ; Business ; Mathematics ; Computer security Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Pesticide Residue Analysis and Safety
Authors: Thomas C. Sparks | Gary D. Crouse | Zoltán Benkő | David A. Demeter | Natalie C. Giampietro | William T. Lambert | Annette Vitale Brown
Source: Pest Management Science (review, 2020-09-27)
Field: Chemistry — Organic Chemistry

The spinosyns, spinosad, spinetoram, and synthetic spinosyn mimics ‐ discovery, exploration, and evolution of a natural product chemistry and the impact of computational tools Natural products (NPs) have long been a source of insecticidal crop protection products. Like many macrolide NPs, the spinosyns originated from a soil inhibiting microorganism (Saccharopolyspora spinosa). More than 20 years after initial registration, the spinosyns remain a unique class of NP-based insect control products that presently encompass two insecticidal active ingredients, spinosad, a naturally occurring mixture of spinosyns, and spinetoram, a semi-synthetic spinosyn product. The exploration and exploitation of the spinosyns has, unusually, been tied to an array of computational tools including artificial intelligence (AI)-based quantitative structure activity relationship (QSAR) and most recently computer-aided modeling and design (CAMD). The AI-based QSAR directly lead to the discovery of spinetoram, while the CAMD studies have recently resulted in the discovery and building of a series of synthetic spinosyn mimics. The most recent of these synthetic spinosyn mimics show promise as insecticides targeting lepidopteran insect pests as demonstrated by field studies wherein the efficacy has been shown to be comparable to spinosad and spinetoram. These and a range of other aspects related to the exploration of the spinosyns over the past 30 years are reviewed herein. © 2020 Society of Chemical Industry. Spinosad ; Natural product ; Chemistry ; Natural (archaeology) ; Biochemical engineering ; Computational biology ; Biology ; Biochemistry ; Engineering ; Ecology ; Pesticide Chemical synthesis and alkaloids ; Microbial Natural Products and Biosynthesis ; Phytochemical compounds biological activities