jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruihao Yuan | Zhen Liu | Prasanna V. Balachandran | Deqing Xue | Yumei Zhou | Xiangdong Ding | Jun Sun | Dezhen Xue | Turab Lookman
Source: Advanced Materials (article, 2018-01-08)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Large Electrostrains in BaTiO<sub>3</sub>‐Based Piezoelectrics Using Active Learning A key challenge in guiding experiments toward materials with desired properties is to effectively navigate the vast search space comprising the chemistry and structure of allowed compounds. Here, it is shown how the use of machine learning coupled to optimization methods can accelerate the discovery of new Pb-free BaTiO<sub>3</sub> (BTO-) based piezoelectrics with large electrostrains. By experimentally comparing several design strategies, it is shown that the approach balancing the trade-off between exploration (using uncertainties) and exploitation (using only model predictions) gives the optimal criterion leading to the synthesis of the piezoelectric (Ba<sub>0.84</sub> Ca<sub>0.16</sub> )(Ti<sub>0.90</sub> Zr<sub>0.07</sub> Sn<sub>0.03</sub> )O<sub>3</sub> with the largest electrostrain of 0.23% in the BTO family. Using Landau theory and insights from density functional theory, it is uncovered that the observed large electrostrain is due to the presence of Sn, which allows for the ease of switching of tetragonal domains under an electric field. Piezoelectricity ; Materials science ; Tetragonal crystal system ; Key (lock) ; Field (mathematics) ; Density functional theory ; Space (punctuation) ; Nanotechnology ; Electric field ; Engineering physics ; Computer science ; Composite material ; Physics ; Crystal structure ; Crystallography ; Mathematics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Dhiren K. Pradhan | Shalini Kumari | Rama K. Vasudevan | Evgheni Strelcov | Venkata Sreenivas Puli | Dillip K. Pradhan | Ashok Kumar | J. M. Gregg | A. K. Pradhan | Sergei V. Kalinin | Ram S. Katiyar
Source: Scientific Reports (article, 2018-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the Magnetoelectric Coupling at the Composite Interfaces of FE/FM/FE Heterostructures Multiferroic materials have attracted considerable attention as possible candidates for a wide variety of future microelectronic and memory devices, although robust magnetoelectric (ME) coupling between electric and magnetic orders at room temperature still remains difficult to achieve. In order to obtain robust ME coupling at room temperature, we studied the Pb(Fe<sub>0.5</sub>Nb<sub>0.5</sub>)O<sub>3</sub>/Ni<sub>0.65</sub>Zn<sub>0.35</sub>Fe<sub>2</sub>O<sub>4</sub>/Pb(Fe<sub>0.5</sub>Nb<sub>0.5</sub>)O<sub>3</sub> (PFN/NZFO/PFN) trilayer structure as a representative FE/FM/FE system. We report the ferroelectric, magnetic and ME properties of PFN/NZFO/PFN trilayer nanoscale heterostructure having dimensions 70/20/70 nm, at room temperature. The presence of only (00l) reflection of PFN and NZFO in the X-ray diffraction (XRD) patterns and electron diffraction patterns in Transmission Electron Microscopy (TEM) confirm the epitaxial growth of multilayer heterostructure. The distribution of the ferroelectric loop area in a wide area has been studied, suggesting that spatial variability of ferroelectric switching behavior is low, and film growth is of high quality. The ferroelectric and magnetic phase transitions of these heterostructures have been found at ~575 K and ~650 K, respectively which are well above room temperature. These nanostructures exhibit low loss tangent, large saturation polarization (P<sub>s</sub> ~ 38 µC/cm<sup>2</sup>) and magnetization (M<sub>s</sub> ~ 48 emu/cm<sup>3</sup>) with strong ME coupling at room temperature revealing them as potential candidates for nanoscale multifunctional and spintronics device applications. Materials science ; Ferroelectricity ; Multiferroics ; Heterojunction ; Spintronics ; Condensed matter physics ; Curie temperature ; Optoelectronics ; Magnetization ; Ferromagnetism ; Nanotechnology ; Dielectric ; Magnetic field ; Physics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics
Authors: Kevin Ryan | Jeff Lengyel | Michael Shatruk
Source: Journal of the American Chemical Society (article, 2018-06-06)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction via Deep Learning We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multiperspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire data set of ∼50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known crystal structures in order to predict the likelihood of forming new compounds that could be generated by placing elements into these structural templates in a combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, which was applied to a test set of structures never seen by the model during training, indicates its ability to predict known elemental compositions with a high likelihood of success. In ∼30% of cases, the known compositions were found among the top 10 most likely candidates proposed by the model. These results suggest that the approach developed in this work can be used to effectively guide the synthetic efforts in the discovery of new materials, especially in the case of systems composed of three or more chemical elements. Template ; Artificial neural network ; Crystal structure prediction ; Chemistry ; Set (abstract data type) ; Crystal (programming language) ; Crystal structure ; Artificial intelligence ; Periodic table ; Topology (electrical circuits) ; Data set ; Machine learning ; Computer science ; Algorithm ; Biological system ; Crystallography ; Mathematics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Tristan Petit | Ljiljana Puškar
Source: Diamond and Related Materials (article, 2018-08-14)
Field: Materials Science — Materials Chemistry

FTIR spectroscopy of nanodiamonds: Methods and interpretation Fourier transform infrared spectroscopy ; Characterization (materials science) ; Materials science ; Fourier transform spectroscopy ; Spectroscopy ; Nanodiamond ; Infrared spectroscopy ; Fourier transform ; Nanotechnology ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Physics ; Organic chemistry ; Diamond ; Composite material Diamond and Carbon-based Materials Research ; Advanced materials and composites ; High-pressure geophysics and materials
Authors: Lian Xiao | Handong Sun
Source: Nanoscale Horizons (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Novel properties and applications of carbon nanodots In the most recent decade, carbon dots have drawn intensive attention and triggered substantial investigation. Carbon dots manifest superior merits, including excellent biocompatibility both in vitro and in vivo, resistance to photobleaching, easy surface functionalization and bio-conjugation, outstanding colloidal stability, eco-friendly synthesis, and low cost. All of these endow them with the great potential to replace conventional unsatisfactory fluorescent heavy metal-containing semiconductor quantum dots or organic dyes. Even though the understanding of their photoluminescence mechanism is still controversial, carbon dots have already exhibited many versatile applications. In this article, we summarize and review the recent progress achieved in the field of carbon dots, and provide a comprehensive summary and discussion on their synthesis methods and emission mechanisms. We also present the applications of carbon dots in bioimaging, drug delivery, microfluidics, light emitting diode (LED), sensing, logic gates, and chiral photonics, etc. Some unaddressed issues, challenges, and future prospects of carbon dots are also discussed. We envision that carbon dots will eventually have great commercial utilization and will become a strong competitor to some currently used fluorescent materials. It is our hope that this review will provide insights into both the fundamental research and practical applications of carbon dots. Nanodot ; Nanotechnology ; Materials science ; Carbon fibers ; Optoelectronics ; Composite material Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Simon Steinberg | Richard Dronskowski
Source: Crystals (article, 2018-05-18)
Field: Chemistry — Inorganic Chemistry

The Crystal Orbital Hamilton Population (COHP) Method as a Tool to Visualize and Analyze Chemical Bonding in Intermetallic Compounds Recognizing the bonding situations in chemical compounds is of fundamental interest for materials design because this very knowledge allows us to understand the sheer existence of a material and the structural arrangement of its constituting atoms. Since its definition 25 years ago, the Crystal Orbital Hamilton Population (COHP) method has been established as an efficient and reliable tool to extract the chemical-bonding information based on electronic-structure calculations of various quantum-chemical types. In this review, we present a brief introduction into the theoretical background of the COHP method and illustrate the latter by diverse applications, in particular by looking at representatives of the class of (polar) intermetallic compounds, usually considered as “black sheep” in the light of valence-electron counting schemes. Intermetallic ; Chemical bond ; Valence electron ; Population ; Quantum chemical ; Valence (chemistry) ; Crystal structure ; Crystal (programming language) ; Chemical physics ; Materials science ; Computational chemistry ; Chemistry ; Crystallography ; Computer science ; Electron ; Physics ; Molecule ; Quantum mechanics ; Metallurgy ; Organic chemistry Inorganic Chemistry and Materials ; Crystal Structures and Properties ; Crystallography and molecular interactions
Authors: Bob W.J. Pirok | Dwight R. Stoll | Peter J. Schoenmakers
Source: Analytical Chemistry (article, 2018-11-01)
Field: Chemistry — Spectroscopy

Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTRecent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical ApplicationsBob W. J. Pirok*Bob W. J. PirokUniversity of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The NetherlandsTI-COAST, Science Park 904, 1098 XH Amsterdam, The Netherlands*E-mail: [email protected]More by Bob W. J. PirokView Biographyhttp://orcid.org/0000-0002-4558-3778, Dwight R. StollDwight R. StollDepartment of Chemistry, Gustavus Adolphus College, Saint Peter, Minnesota 56082, United StatesMore by Dwight R. StollView Biographyhttp://orcid.org/0000-0002-4070-9132, and Peter J. SchoenmakersPeter J. SchoenmakersUniversity of Amsterdam, van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The NetherlandsMore by Peter J. SchoenmakersView BiographyCite this: Anal. Chem. 2019, 91, 1, 240–263Publication Date (Web):November 1, 2018Publication History Published online1 November 2018Published inissue 2 January 2019https://pubs.acs.org/doi/10.1021/acs.analchem.8b04841https://doi.org/10.1021/acs.analchem.8b04841review-articleACS PublicationsCopyright © 2018 American Chemical Society. This publication is licensed under CC-BY-NC-ND. This publication is Open Access under the license indicated. Learn MoreArticle Views13313Altmetric-Citations247LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (8 MB) Get e-AlertscloseSUBJECTS:Biopolymers,Chromatography,Peptides and proteins,Size exclusion chromatography,Solvents Get e-Alerts Chemistry ; Chromatography ; Biochemical engineering Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Protein purification and stability
Authors: Hari Sharan Adhikari | Paras Nath Yadav
Source: International Journal of Biomaterials (review, 2018-12-30)
Field: Materials Science — Biomaterials

Anticancer Activity of Chitosan, Chitosan Derivatives, and Their Mechanism of Action Tailoring of chitosan through the involvement of its amino, acetamido, and hydroxy groups can give derivatives of enhanced solubility and remarkable anticancer activity. The general mechanism of such activity is associated with the disturbances in normal functioning of cell cycle, interference to the central dogma of biological system from DNA to RNA to protein or enzymatic synthesis, and the disruption of hormonal path to biosynthesis to inhibit the growth of cancer cells. Both chitosan and its various derivatives have been reported to selectively permeate through the cancer cell membranes and show anticancer activity through the cellular enzymatic, antiangiogenic, immunoenhancing, antioxidant defense mechanism, and apoptotic pathways. They get sequestered from noncancer cells and provide their enhanced bioavailability in cancer cells in a sustained release manner. This review presents the putative mechanisms of anticancer activity of chitosan and mechanistic approaches of structure activity relation upon the modification of chitosan through functionalization, complex formation, and graft copolymerization to give different derivatives. Chitosan ; Chemistry ; Mechanism of action ; Antioxidant ; Cancer cell ; Biochemistry ; Apoptosis ; Pharmacology ; Cell biology ; Cancer ; Biology ; In vitro Nanoparticle-Based Drug Delivery ; Click Chemistry and Applications ; RNA Interference and Gene Delivery
Authors: Xing Li | Qiang Gao | Juefan Wang | Yifeng Chen | Zhihui Chen | Hai‐Sen Xu | Wei Tang | Kai Leng | Guo‐Hong Ning | Jishan Wu | Qing‐Hua Xu | Su Ying Quek | Yixin Lü | Kian Ping Loh
Source: Nature Communications (article, 2018-06-07)
Field: Materials Science — Materials Chemistry

Tuneable near white-emissive two-dimensional covalent organic frameworks Most two-dimensional (2D) covalent organic frameworks (COFs) are non-fluorescent in the solid state even when they are constructed from emissive building blocks. The fluorescence quenching is usually attributed to non-irradiative rotation-related or π-π stacking-caused thermal energy dissipation process. Currently there is a lack of guiding principle on how to design fluorescent, solid-state material made of COF. Herein, we demonstrate that the eclipsed stacking structure of 2D COFs can be used to turn on, and tune, the solid-state photoluminescence from non-emissive building blocks by the restriction of intramolecular bond rotation via intralayer and interlayer hydrogen bonds among highly organized layers in the eclipse-stacked COFs. Our COFs serve as a platform whereby the size of the conjugated linkers and side-chain functionalities can be varied, rendering the emission colour-tuneable from blue to yellow and even white. This work provides a guide to design new solid-state emitters using COFs. Stacking ; Intramolecular force ; Materials science ; Fluorescence ; Covalent bond ; Hydrogen bond ; Nanotechnology ; Conjugated system ; Förster resonance energy transfer ; Photoluminescence ; Solid-state ; Optoelectronics ; Polymer ; Chemistry ; Molecule ; Optics ; Physics ; Physical chemistry Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Aria Mansouri Tehrani | Anton O. Oliynyk | Marcus Parry | Zeshan Rizvi | Samantha Couper | Feng Lin | Lowell Miyagi | Taylor D. Sparks | Jakoah Brgoch
Source: Journal of the American Chemical Society (article, 2018-07-16)
Field: Materials Science — Materials Chemistry

Machine Learning Directed Search for Ultraincompressible, Superhard Materials In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct the synthetic efforts toward compounds with high hardness by predicting the elastic moduli as a proxy. This approach screens 118 287 compounds compiled in crystal structure databases for the materials with the highest bulk and shear moduli determined by support vector machine regression. Following these models, a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide are selected and synthesized at ambient pressure. High-pressure diamond anvil cell measurements corroborate the machine-learning prediction of the bulk modulus with less than 10% error, as well as confirm the ultraincompressible nature of both compounds. Subsequent Vickers microhardness measurements reveal that each compound also has an extremely high hardness exceeding the superhard threshold of 40 GPa at low loads (0.49 N). These results show the effectiveness of materials development through state-of-the-art machine-learning techniques by identifying functional inorganic materials. Bulk modulus ; Tungsten carbide ; Ternary operation ; Elastic modulus ; Vickers hardness test ; Indentation hardness ; Tungsten ; Chemistry ; Diamond anvil cell ; Shear modulus ; Machine learning ; Composite material ; Metallurgy ; High pressure ; Materials science ; Crystallography ; Thermodynamics ; Computer science Boron and Carbon Nanomaterials Research ; Advanced materials and composites ; Machine Learning in Materials Science
Authors: Kirstin Alberi | Marco Buongiorno Nardelli | Andriy Zakutayev | Luboš Mitáš | Stefano Curtarolo | Anubhav Jain | Marco Fornari | Nicola Marzari | Ichiro Takeuchi | Martin L. Green | Mercouri G. Kanatzidis | Michael F. Toney | Sergiy Butenko | Bryce Meredig | Stephan Lany | Ursula R. Kattner | Albert V. Davydov | Eric S. Toberer | Vladan Stevanović | Aron Walsh | Nam‐Gyu Park | Alán Aspuru‐Guzik | Daniel P. Tabor | Jenny Nelson | James E. Murphy | Anant Setlur | John M. Gregoire | Hong Li | Ruijuan Xiao | Alfred Ludwig | Lane W. Martin | Andrew M. Rappe | Su‐Huai Wei | John D. Perkins
Source: Journal of Physics D Applied Physics (article, 2018-08-09)
Field: Materials Science — Materials Chemistry

The 2019 materials by design roadmap Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design. Characterization (materials science) ; Systems engineering ; Computer science ; Renewable energy ; Nanotechnology ; Materials informatics ; Engineering ; Materials science ; Health informatics ; Health care Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Electronic and Structural Properties of Oxides
Authors: Ya Zhuo | Aria Mansouri Tehrani | Anton O. Oliynyk | Anna Duke | Jakoah Brgoch
Source: Nature Communications (article, 2018-10-16)
Field: Materials Science — Materials Chemistry

Identifying an efficient, thermally robust inorganic phosphor host via machine learning Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure's Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB<sub>9</sub>O<sub>15</sub> shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B<sub>3</sub>O<sub>7</sub>]<sup>5-</sup> polyanionic backbone. Substituting this material with Eu<sup>2+</sup> yields UV excitation bands and a narrow violet emission at 416 nm with a full-width at half-maximum of 34.5 nm. More importantly, NaBaB<sub>9</sub>O<sub>15</sub>:Eu<sup>2+</sup> possesses a quantum yield of 95% and excellent thermal stability. Phosphor ; Host (biology) ; Computer science ; Host response ; Materials science ; Biology ; Optoelectronics ; Ecology ; Immune system ; Genetics Luminescence Properties of Advanced Materials ; Machine Learning in Materials Science ; Radiation Detection and Scintillator Technologies