jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Artem Maksov | Ondrej Dyck | Kai Wang | Kai Xiao | David B. Geohegan | Bobby G. Sumpter | Rama K. Vasudevan | Stephen Jesse | Sergei V. Kalinin | Maxim Ziatdinov
Source: npj Computational Materials (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2 Abstract Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature, with atomic resolution. However, despite the ever-expanding capabilities for high-resolution data acquisition, the inferred information about kinetics and thermodynamics of the process, and single defect dynamics and interactions is minimal. This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data. To circumvent this problem, we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS 2 . The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds, which are then classified into different categories using unsupervised clustering methods. We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy, providing insight into point-defect dynamics and reactions. This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level. Dopant ; Visualization ; Lattice (music) ; Cluster analysis ; Scanning transmission electron microscopy ; Phase diagram ; Deep learning ; Phase transition Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Ana Balea | Elena Fuente | Ángeles Blanco | Carlos Negro
Source: Polymers (review, 2019-03-19)
Field: Materials Science — Biomaterials

Nanocelluloses: Natural-Based Materials for Fiber-Reinforced Cement Composites. A Critical Review Nanocelluloses (NCs) are bio-based nano-structurated products that open up new solutions for natural material sciences. Although a high number of papers have described their production, properties, and potential applications in multiple industrial sectors, no review to date has focused on their possible use in cementitious composites, which is the aim of this review. It describes how they could be applied in the manufacturing process as a raw material or an additive. NCs improve mechanical properties (internal bonding strength, modulus of elasticity (MOE), and modulus of rupture (MOR)), alter the rheology of the cement paste, and affect the physical properties of cements/cementitious composites. Additionally, the interactions between NCs and the other components of the fiber cement matrix are analyzed. The final result depends on many factors, such as the NC type, the dosage addition mode, the dispersion, the matrix type, and the curing process. However, all of these factors have not been studied in full so far. This review has also identified a number of unexplored areas of great potential for future research in relation to NC applications for fiber-reinforced cement composites, which will include their use as a surface treatment agent, an anionic flocculant, or an additive for wastewater treatment. Although NCs remain expensive, the market perspective is very promising. Materials science ; Composite material ; Cement ; Rheology ; Curing (chemistry) ; Cementitious ; Young's modulus ; Raw material ; Nanofiber ; Fiber Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites
Authors: A. Miguel
Source: Physical review. B./Physical review. B (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bart\'ok et al., Phys. Rev. B 87, 184115 (2013).]. Our aim is to improve the computational efficiency of SOAP-based similarity kernel construction. While these improved atomic descriptors can be used for general characterization and interpolation of atomic properties, their main target application is accelerated evaluation of machine-learning-based interatomic potentials within the Gaussian approximation potential (GAP) framework [Bart\'ok et al., Phys. Rev. Lett. 104, 136403 (2010)]. We achieve this objective by expressing the atomic densities in an approximate separable form, which decouples the radial and angular channels. We then express the elements of the SOAP descriptor (i.e., the expansion coefficients for the atomic densities) in analytical form given a particular choice of radial basis set. Finally, we derive recursion formulas for the expansion coefficients. This new SOAP-based descriptor allows for tenfold speedups compared to previous implementations, while improving the stability of the radial expansion for distant atomic neighbors, without degradation of the interpolation power of GAP models. Interpolation (computer graphics) ; Kernel (algebra) ; Gaussian ; Set (abstract data type) ; Stability (learning theory) ; Basis set ; Computer science ; Physics ; Algorithm ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Machine learning ; Density functional theory ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Anmin Nie | Yeqiang Bu | Penghui Li | Yizhi Zhang | Tianye Jin | Jiabin Liu | Su Zhang | Yanbin Wang | Julong He | Zhongyuan Liu | Hongtao Wang | Yongjun Tian | Wei Yang
Source: Nature Communications (article, 2019-12-04)
Field: Materials Science — Materials Chemistry

Approaching diamond’s theoretical elasticity and strength limits Diamond is the hardest natural material, but its practical strength is low and its elastic deformability extremely limited. While recent experiments have demonstrated that diamond nanoneedles can sustain exceptionally large elastic tensile strains with high tensile strengths, the size- and orientation-dependence of these properties remains unknown. Here we report maximum achievable tensile strain and strength of diamond nanoneedles with various diameters, oriented in <100>, <110> and <111> -directions, using in situ transmission electron microscopy. We show that reversible elastic deformation depends both on nanoneedle diameter and orientation. <100> -oriented nanoneedles with a diameter of 60 nm exhibit highest elastic tensile strain (13.4%) and tensile strength (125 GPa). These values are comparable with the theoretical elasticity and Griffith strength limits of diamond, respectively. Our experimental data, together with first principles simulations, indicate that maximum achievable elastic strain and strength are primarily determined by surface conditions of the nanoneedles. Ultimate tensile strength ; Diamond ; Materials science ; Elasticity (physics) ; Nanoneedle ; Transmission electron microscopy ; Composite material ; Nanotechnology ; Nanostructure Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Moritz Heusel | Isabell Bludau | George Rosenberger | Robin Hafen | Max Frank | Amir Banaei‐Esfahani | Audrey van Drogen | Ben C. Collins | Matthias Gstaiger | Ruedi Aebersold
Source: Molecular Systems Biology (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Complex‐centric proteome profiling by <scp>SEC</scp> ‐ <scp>SWATH</scp> ‐ <scp>MS</scp> Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework <i>CCprofiler</i> to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub-complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset <i>CCprofiler</i> freely accessible and provide a web platform, <i>SECexplorer</i>, for custom exploration of the HEK293 proteome modularity. Biology ; Proteome ; Computational biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: İlkay Çınar | Murat Köklü
Source: International Journal of Intelligent Systems and Applications in Engineering (article, 2019-09-30)
Field: Chemistry — Analytical Chemistry

Classification of Rice Varieties Using Artificial Intelligence Methods Rice being one of the most widely produced and consumed cereal crops in the world, is also the one of the main sustenance in our country because of its economical and nutritious nature. Rice, starting from farm to our table, goes through some manufacturing steps such as a cleaning process, color sorting and classification. If these stages are to be mentioned briefly, cleaning is the process of separating rice from foreign substances, classification is the process of separating broken ones with sturdy ones; color extraction is the process of separating the stained and striped ones except the whiteness on the rice surface. In this study, a computerized vision system was developed in order to distinguish between two proprietary rice species. A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice. With these features, models were created using LR, MLP, SVM, DT, RF, NB and k-NN machine learning techniques and performance measurement values were obtained. Success rates in the classification were obtained 93.02% (LR), 92.86% (MLP), 92.83% (SVM), 92.49% (DT), 92.39% (RF), 91.71% (NB), 88.58% (k-NN). When we look at the results of the success rate of obtain, it is possible to say that the study achieved success. Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; GABA and Rice Research
Authors: Hyeon‐Ho Jeong | Eunjin Choi | Elizabeth Ellis | Tung‐Chun Lee
Source: Journal of Materials Chemistry B (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in gold nanoparticles for biomedical applications: from hybrid structures to multi-functionality Hybrid gold nanoparticles for biomedical applications are reviewed in the context of a novel classification framework and illustrated by recent examples. Nanotechnology ; Colloidal gold ; Nanoparticle ; Materials science ; Context (archaeology) ; Computer science ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications
Authors: Xiaojiao Cai | Yun Hang Hu
Source: Energy Science & Engineering (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Advances in catalytic conversion of methane and carbon dioxide to highly valuable products Abstract Two typical processes have been developed for the conversion of methane and carbon dioxide to higher valuable products: dry reforming of methane ( DRM ) and CO 2 ‐oxidative coupling of methane ( CO 2 ‐ OCM ). Numerous articles reviewed progresses in either DRM or CO 2 ‐ OCM , but no one covers both processes. In this review article, we systematically evaluated progresses in both DRM and CO 2 ‐ OCM processes for conversion of methane and CO 2 to highly valuable products. Critical issues, which are carbon deposition and high energy cost for DRM and the contradiction between large activity and high selectivity for CO 2 ‐ OCM , were emphasized. Strategies to develop effective catalysts were evaluated, including the enhancement of metal‐support interactions, the introduction of promotors, the formation of solid solutions, and the construction of core‐shell structures. Plasma and photocatalysis were discussed as new promising technologies for DRM and CO 2 ‐ OCM . Methane ; Oxidative coupling of methane ; Catalysis ; Carbon dioxide reforming ; Carbon dioxide ; Carbon fibers ; Chemistry ; Syngas ; Photocatalysis ; Nanotechnology ; Chemical engineering ; Environmental science ; Materials science ; Process engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Mirjana B. Čolović | Milan Lacković | Jovana Lalatović | Ali S. Mougharbel | Ulrich Kortz | Danijela Krstić
Source: Current Medicinal Chemistry (review, 2019-08-27)
Field: Materials Science — Materials Chemistry

Polyoxometalates in Biomedicine: Update and Overview Although a wide range of biological activities of Polyoxometalates (POMs) has been reported, they are to the best of our knowledge not close to a clinical trial or a final application in the treatment of diabetes or infectious and malignant diseases. Accordingly, further studies should be directed towards determining the mechanism of POM biological actions, which would enable fine-tuning at the molecular level, and consequently efficient action towards biological targets and as low toxicity as possible. Furthermore, biomedical studies should be performed on solutionstable POMs employing physiological conditions and concentrations. Biomedicine ; MEDLINE ; Medicine ; Data science ; Computer science ; Chemistry ; Bioinformatics ; Biology ; Biochemistry Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions ; Sulfur-Based Synthesis Techniques
Authors: Nicholas R. Jaegers | Jun‐Kun Lai | Yang He | Éric Walter | David A. Dixon | Monica Vasiliu | Ying Chen | Chongmin Wang | Mary Y. Hu | Karl T. Mueller | Israel E. Wachs | Yong Wang | Jian Zhi Hu
Source: Angewandte Chemie International Edition (review, 2019-07-08)
Field: Materials Science — Materials Chemistry

Mechanism by which Tungsten Oxide Promotes the Activity of Supported V<sub>2</sub>O<sub>5</sub>/TiO<sub>2</sub> Catalysts for NO<sub><i>X</i></sub> Abatement: Structural Effects Revealed by <sup>51</sup>V MAS NMR Spectroscopy The selective catalytic reduction (SCR) of NO<sub>x</sub> with NH<sub>3</sub> to N<sub>2</sub> with supported V<sub>2</sub> O<sub>5</sub> (-WO<sub>3</sub> )/TiO<sub>2</sub> catalysts is an industrial technology used to mitigate toxic emissions. Long-standing uncertainties in the molecular structures of surface vanadia are clarified, whereby progressive addition of vanadia to TiO<sub>2</sub> forms oligomeric vanadia structures and reveals a proportional relationship of SCR reaction rate to [surface VO<sub>x</sub> concentration]<sup>2</sup> , implying a 2-site mechanism. Unreactive surface tungsta (WO<sub>3</sub> ) also promote the formation of oligomeric vanadia (V<sub>2</sub> O<sub>5</sub> ) sites, showing that promoter incorporation enhances the SCR reaction by a structural effect generating adjacent surface sites and not from electronic effects as previously proposed. The findings outline a method to assess structural effects of promoter incorporation on catalysts and reveal both the dual-site requirement for the SCR reaction and the important structural promotional effect that tungsten oxide offers for the SCR reaction by V<sub>2</sub> O<sub>5</sub> /TiO<sub>2</sub> catalysts. Tungsten ; Catalysis ; Oxide ; Mechanism (biology) ; Materials science ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Metallurgy ; Physics ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Transition Metal Oxide Nanomaterials
Authors: Chiho Kim | Anand Chandrasekaran | Anurag Jha | Rampi Ramprasad
Source: MRS Communications (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Active-learning and materials design: the example of high glass transition temperature polymers Materials science ; Glass transition ; Polymer ; Composite material ; Engineering physics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Nanowire Synthesis and Applications
Authors: Cheng Zhan | Weiwei Sun | Yu Xie | De‐en Jiang | Paul R. C. Kent
Source: ACS Applied Materials & Interfaces (article, 2019-05-13)
Field: Materials Science — Materials Chemistry

Computational Discovery and Design of MXenes for Energy Applications: Status, Successes, and Opportunities MXenes (M<sub><i>n</i>+1</sub>X<sub><i>n</i></sub>, e.g., Ti<sub>3</sub>C<sub>2</sub>) are the largest 2D material family developed in recent years. They exhibit significant potential in the energy sciences, particularly for energy storage. In this review, we summarize the progress of the computational work regarding the theoretical design of new MXene structures and predictions for energy applications including their fundamental, energy storage, and catalytic properties. We also outline how high-throughput computation, big data, and machine-learning techniques can help broaden the MXene family. Finally, we present some of the major remaining challenges and future research directions needed to mature this novel materials family. MXenes ; Work (physics) ; Energy (signal processing) ; Energy storage ; Throughput ; Nanotechnology ; Materials science ; Computer science ; Data science ; Engineering ; Mechanical engineering ; Telecommunications ; Physics ; Wireless MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Zdeněk Jakub | Jan Hulva | Matthias Meier | Roland Bliem | Florian Kraushofer | Martin Setvín | Michael Schmid | Ulrike Diebold | Cesare Franchini | Gareth S. Parkinson
Source: Angewandte Chemie International Edition (review, 2019-07-24)
Field: Materials Science — Materials Chemistry

Local Structure and Coordination Define Adsorption in a Model Ir<sub>1</sub>/Fe<sub>3</sub>O<sub>4</sub> Single‐Atom Catalyst Single-atom catalysts (SACs) bridge homo- and heterogeneous catalysis because the active site is a metal atom coordinated to surface ligands. The local binding environment of the atom should thus strongly influence how reactants adsorb. Now, atomically resolved scanning-probe microscopy, X-ray photoelectron spectroscopy, temperature-programmed desorption, and DFT are used to study how CO binds at different Ir<sub>1</sub> sites on a precisely defined Fe<sub>3</sub> O<sub>4</sub> (001) support. The two- and five-fold-coordinated Ir adatoms bind CO more strongly than metallic Ir, and adopt structures consistent with square-planar Ir<sup>I</sup> and octahedral Ir<sup>III</sup> complexes, respectively. Ir incorporates into the subsurface already at 450 K, becoming inactive for adsorption. Above 900 K, the Ir adatoms agglomerate to form nanoparticles encapsulated by iron oxide. These results demonstrate the link between SAC systems and coordination complexes, and that incorporation into the support is an important deactivation mechanism. Atom (system on chip) ; Catalysis ; Adsorption ; Coordination number ; Crystallography ; Materials science ; Chemistry ; Physical chemistry ; Computer science ; Ion Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: John Ozdemir | Imann Mosleh | Mojtaba Abolhassani | Lauren F. Greenlee | Robert Beitle | M. Hassan Beyzavi
Source: Frontiers in Energy Research (article, 2019-09-13)
Field: Materials Science — Materials Chemistry

Covalent Organic Frameworks for the Capture, Fixation, or Reduction of CO2 Covalent organic frameworks (COFs) are porous crystalline organic polymers which have been the subject of immense research interest in the past 10 years. COF materials are synthesized by the covalent linkage of organic molecules bonded in a repeating fashion to form a porous crystal that is ideal for gas adsorption and storage. Chemists have strategically designed COFs for the purpose of heterogeneous catalysis of gaseous reactants. Presented in this critical review are efforts toward developing COFs for the sequestration of CO 2 from the atmosphere. Researchers have determined the CO 2 adsorption capabilities of several COFs is competitive with the highest surface area materials. Engineering the pore environment of COFs with chemical moieties that interact with CO 2 have increased the CO 2 adsorption performance. The installation of CO 2 binding moieties in the COF has made possible the selective adsorption of CO 2 over other gases such as N 2 . The high degree of control of internal pore composition in COFs is coupled with high CO 2 adsorption to develop heterogeneous catalysts for the conversion of CO 2 to value added products. Two notable examples of this catalysis are the fixation of CO 2 to epoxides for the synthesis of cyclic carbonates and the reduction of CO 2 to CO. Recent examples of COFs for the capture of CO 2 will be discussed followed by COF catalysts which use CO 2 as a feedstock for the production of value-added products. Adsorption ; Catalysis ; Chemical engineering ; Porosity ; Materials science ; Polymer ; Covalent bond ; Chemistry ; Nanotechnology ; Organic chemistry ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies