jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Szabolcs Horvát | Adeeba Fathima | Stefan Görlich | Carl D. Modes | Michael Schlierf | Nils Kröger
Source: Research Square (preprint, 2020-12-28)
Field: Materials Science — Biomaterials

Computational Analysis of The Effects of Nitrogen Source and Sin1 Knockout on Biosilica Morphology in The Model Diatom Thalassiosira Pseudonana Abstract Morphogenesis of the silica based cell walls of diatoms, a large group of microalgae, is a paradigm for the self-assembly of complex 3D nano- and microscale patterned inorganic materials. In recent years, loss-of-function studies using genetic manipulation were successfully applied for the identification of genes that guide silica morphogenesis in diatoms. These studies revealed that the loss of one gene can affect multiple morphological parameters, and the morphological changes can be rather subtle being blurred by natural variations in morphology even within the same clone. Both factors severely hamper the identification of morphological mutants using subjective by-eye inspection of electron micrographs. Here we have developed automated image analysis for objectively quantifying the morphology of ridge networks and pore densities from numerous electron micrographs of diatom biosilica. This study demonstrated differences in ridge network morphology and pore density in diatoms growing on ammonium rather than nitrate as sole nitrogen source. Furthermore, it revealed shortcomings in previous by-eye evaluation of the biosilica phenotype of the silicanin-1 knockout mutant. We anticipate that the computational methods established in the present work, will be invaluable for unraveling genotype-phenotype correlations in diatom biosilica morphogenesis. Diatom ; Morphogenesis ; Thalassiosira pseudonana ; Morphology (biology) ; Phenotype ; Biology ; Frustule ; Nucleation ; Mutant ; Gene ; Botany ; Chemistry ; Genetics ; Ecology ; Nutrient Diatoms and Algae Research ; Algal biology and biofuel production ; Microbial Community Ecology and Physiology
Authors: et al. A. Parre | TJPRC
Source: International journal of mechanical and production engineering research and development (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Investigation On Three Point Flexural Strength Of Alkaline Treated Banana Fiber Reinforced Cardanol Resin Polymer Composites By Response Surface Methodology to attain the optimum flexural strength of 47.25 MPa for our ATBF/CR Biocomposites. Cardanol ; Composite material ; Flexural strength ; Materials science ; Epoxy Natural Fiber Reinforced Composites
Authors: Martín Leandro Paleico | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2020-02-03)
Field: Materials Science — Materials Chemistry

A flexible and adaptive grid algorithm for global optimization utilizing\n basin hopping Monte Carlo Global optimization is an active area of research in atomistic simulations,\nand many algorithms have been proposed to date. A prominent example is basin\nhopping Monte Carlo, which performs a modified Metropolis Monte Carlo search to\nexplore the potential energy surface of the system of interest. These\nsimulations can be very demanding due to the high-dimensional configurational\nsearch space. The effective search space can be reduced by utilizing grids for\nthe atomic positions, but at the cost of possibly biasing the results if fixed\ngrids are employed. In this paper, we present a flexible grid algorithm for\nglobal optimization that allows to exploit the efficiency of grids without\nbiasing the simulation outcome. The method is general and applicable to very\nheterogeneous systems, such as interfaces between two materials of different\ncrystal structure or large clusters supported at surfaces. As a benchmark case,\nwe demonstrate its performance for the well-known global optimization problem\nof Lennard-Jones clusters containing up to 100 particles. In spite of the\nsimplicity of this model potential, Lennard-Jones clusters represent a\nchallenging test case, since the global minima for some "magic" numbers of\nparticles exhibit geometries that are very different from those of clusters\nwith only a slightly different size.\n Monte Carlo method ; Maxima and minima ; Global optimization ; Grid ; Computer science ; Benchmark (surveying) ; Statistical physics ; Algorithm ; Mathematical optimization ; Computational science ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Optimization and Search Problems ; Catalytic Processes in Materials Science
Authors: Rodolfo Paula Leite
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Calculation of thermal properties using molecular simulation : Cálculo de propriedades térmicas utilizando simulação molecular excelentes e inspiradores cursos de ps- Simula ; Materials science ; Molecular dynamics ; Thermal ; Thermodynamics ; Chemistry ; Physics ; Computer science ; Computational chemistry Thermal and Kinetic Analysis
Authors: Shunyang Wang | Tobias Kind | Dean J. Tantillo | Oliver Fiehn
Source: Research Square (preprint, 2020-10-20)
Field: Chemistry — Spectroscopy

Predicting in-silico electron ionization mass spectra using quantum chemistry Abstract Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in-silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in-silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale. Ionization ; Chemistry ; Mass spectrum ; In silico ; Electron ; Spectral line ; Quantum chemistry ; Quantum ; Mass spectrometry ; Electron ionization ; Chemical physics ; Computational chemistry ; Atomic physics ; Ion ; Physical chemistry ; Physics ; Chromatography ; Quantum mechanics ; Organic chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Jaehoon Byun | Hee Reyoung Kim
Source: Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (article, 2020-08-21)
Field: Materials Science — Materials Chemistry

Radiological safety assessment of accident scenarios of 1 ton / day spent resin treatment facility Ton ; Radiological weapon ; Waste management ; Medicine ; Forensic engineering ; Engineering ; Surgery ; Geography Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Radiation Dose and Imaging
Authors: J Vegas Olmos | F Cugini | F Buining | N O'mahony | T Truong | L Liss | T Oved | Z Binshtock | D Goldenberg | W Ahmed | Y Wu | R Herrero | K Staliunas | C Alexandropoulos | Skokos | D Scano | L Valcarenghi | K Kondepu | P Castoldi | A Giorgetti | E Ciaramella | G Cossu | E Ertunc | L Gilli | A Messa | M Presi | M Rannello | F Bresciani | V Basso | R Dell'orso | F Palla | M Ruiz | M Richart | A Castro | L Velasco | Y Yao | N Ikeda | T Kuroda | T Mano | H Koyama | Y Sugimoto | K Sakoda | Yi Yu | T Rasmussen | K Yvind | E Semenova | J Mrk | J Osrio | F Amrani | F Delahaye | F Giovanardi | L Vincetti | B Debord | F Grme | F Benabid | I Iglesias-Castreo | M Garrich Alabarce | M Hernndez-Bastida | P Pavn Mario | L Chorchos | O Makarov | M Hinrichs | C Schmidt | B Poddig | J Hilt | P Hellwig | D Schulz | K Bober | J Schostak | R Freund | V Jungnickel | C Economou | T Soukoulis | M Koschny | Kafesaki | F Tani | J Koehler | M Suresh | D Schade | F Kttig | P St | Russell | I Papadakis | A Stathis | N Karampitsos | M Stavrou | D Kyrginas | S Couris | M Emmelmann | M Riegel | E Tangdiongga | A Koonen | J.-P Linnartz | D Behnke | M Mller | P.-B Bk | S Colonques | M Vazquez | A Quirce | A Rosado
Source:  (article, 2020-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Programme UV laser photo-reduction of graphene oxide and graphene Graphene ; Optoelectronics ; Oxide ; Materials science ; Computer science ; Nanotechnology ; Metallurgy Optical Coatings and Gratings ; Photonic and Optical Devices ; Advanced optical system design
Authors: Alexander R Nagle
Source: Research Online (University of Wollongong) (article, 2020-01-01)
Field: Materials Science — Biomaterials

Suspension Near-Field Electrospinning: a Nanofabrication Method of Polymer Nanoarray Architectures for Tissue Engineering Chapter 1. This chapter is divided into six sections. The first will discuss the issue of nerve tissue loss, and the strategies of therapy (1.1). The second describes the role of nanofabrication in tissue engineering (1.2). The third section details the theoretical background of electrospinning in terms of solution and process parameters (1.3). The fourth section introduces near-field electrospinning (NFES), recent advances in this field and the principles of NFES techniques (1.4). The fifth section details objectives for a tissue engineered construct for neural cell therapy, and presents possible viable solutions (1.5). The sixth summarizes the aims and structure of this thesis (1.6)... Electrospinning ; Nanolithography ; Nanotechnology ; Suspension (topology) ; Materials science ; Polymer ; Fabrication ; Composite material Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications
Authors: Stefan Spiegel
Source: Advanced Theory and Simulations (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Another Successful Year for <i>Advanced Theory and Simulations</i> Advanced Theory and Simulations (ATS) was launched only two years ago and already has developed into a fully-fledged publication platform for the highest-quality research on theoretical methods, modeling, and simulation approaches in all areas of natural science and medicine. Citation ; Citation index ; Library science ; Analytics ; Index (typography) ; Computer science ; Table of contents ; Broad spectrum ; Data science ; Operations research ; World Wide Web ; Engineering ; Chemistry Machine Learning in Materials Science
Authors: Reza Maleki | Mohammad Dahri | Hossein Akbarialiabad
Source: Research Square (preprint, 2020-12-14)
Field: Materials Science — Biomaterials

Loading and Release of Anti -Cancer Drugs Using Simultaneous pH and Temperature Responsive Nanohybrid Abstract BackgroundTodays, drug nanocarrier development and improving its biophysical properties is one of the updated and intended of nano-biopharmaceutical science researches. Single-walled carbon nanotubes (SWCNT), as a typical carbon structure based nanocarrier, but have some obstacles in drug delivery mechanisms. In that current study, the penetration, loading, and release of Doxorubicin and Paclitaxel, as two anticancer agents, were investigated using a novel modified and functionalized SWCNT.ResultsThis study was carried out using molecular dynamics simulation based on a dual-responsive smart biomaterial. At the in-silico study, Interaction energies between drugs and carriers, numbers of hydrogen bonds, diffusion coefficient, and gyration radius were investigated. The kinetic analysis of drug adsorption and release revealed that, fascinatingly, drug loading and drug release are selective at physiological and cancerous acidic pH, respectively. Interaction of Dimethyl acryl amid-trimethyl chitosan, as a biodegradable and biocompatible hydrogel, with SWCNT indicated that degradation reaction in acidic condition destructs the polymer, which leads to a smart release in cancerous tissue at specific pH. Moreover, it resolves hydrophilicity, optimum nanoparticle size, cell membrane penetration, and cell toxicity concerns.ConclusionsThe simulation results indicated a marvelous role of dimethyl acryl amide-trimethyl chitosan in the adsorption and release of anticancer drugs in normal and neoplastic tissue. The interaction of trimethyl chitosan also improves biocompatibility as well as biodegradability of the carrier. Overall, that novel drug carrier can be a virtuous nanoparticle for loading, transporting, and releasing the anticancer drugs. Chemistry ; Nanotechnology ; Business ; Chemical engineering ; Materials science ; Engineering Nanoparticle-Based Drug Delivery ; Protein purification and stability ; RNA Interference and Gene Delivery
Authors: Charly Empereur‐mot | Luca Pesce | Davide Bochicchio | Claudio Perego | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2020-07-07)
Field: Materials Science — Biomaterials

Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default&lt;br&gt;settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.&lt;br&gt;Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force&lt;br&gt;fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG. Swarm behaviour ; Particle swarm optimization ; Computer science ; Software ; Parametrization (atmospheric modeling) ; Metaheuristic ; Multi-swarm optimization ; Python (programming language) ; Swarm intelligence ; Algorithm ; Computational science ; Artificial intelligence ; Physics ; Programming language Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Reza Maleki | Mohammad Dahri | Hossein Akbarialiabad
Source: Research Square (Research Square) (preprint, 2020-10-28)
Field: Materials Science — Biomaterials

Loading and Release of Anti-Cancer Drugs Using Simultaneous pH and Temperature Responsive Nanohybrid Abstract Background: Todays, drug nanocarrier development and improving its biophysical properties is one of the updated and intended of nano-biopharmaceutical science researches. Single-walled carbon nanotubes (SWCNT), as a typical carbon structure based nanocarrier, but have some obstacles in drug delivery mechanisms. In that current study, the penetration, loading, and release of Doxorubicin and Paclitaxel, as two anticancer agents, were investigated using a novel modified and functionalized SWCNT. Results: This study was carried out using molecular dynamics simulation based on a dual-responsive smart biomaterial. At the in-silico study, Interaction energies between drugs and carriers, numbers of hydrogen bonds, diffusion coefficient, and gyration radius were investigated. The kinetic analysis of drug adsorption and release revealed that, fascinatingly, drug loading and drug release are selective at physiological and cancerous acidic pH, respectively. Interaction of Dimethyl acryl amid-trimethyl chitosan, as a biodegradable and biocompatible hydrogel, with SWCNT indicated that degradation reaction in acidic condition destructs the polymer, which leads to a smart release in cancerous tissue at specific pH. Moreover, it resolves hydrophilicity, optimum nanoparticle size, cell membrane penetration, and cell toxicity concerns. Conclusions: The simulation results indicated a marvelous role of dimethyl acryl amide-trimethyl chitosan in the adsorption and release of anticancer drugs in normal and neoplastic tissue. The interaction of trimethyl chitosan also improves biocompatibility as well as biodegradability of the carrier. Overall, that novel drug carrier can be a virtuous nanoparticle for loading, transporting, and releasing the anticancer drugs. Nanotechnology ; Cancer ; Chemistry ; Pharmacology ; Materials science ; Biophysics ; Medicine ; Internal medicine ; Biology Nanoparticle-Based Drug Delivery ; Protein purification and stability ; RNA Interference and Gene Delivery
Authors: D Pfau | J Spencer | A De | G Matthews | W Foulkes | Di Luo | B Clark | Y Nomura | A Darmawan | Y Yamaji | M Imada | L Schwarz | A Alavi | G Booth | W Mcmillan | D Ceperley | B Alder | G Carleo | M Troyer | M Morales | J Mcminis | J Kim | G Scuseria | R Feynman | M Cohen | V Pandharipande | N Itoh | M Lee | K Schmidt | M Kalos | G Chester | Y Kwon | R Martin | M Holzmann | C Pierleoni | K Esler | P Lpez Ros | A Ma | N Drummond | M Towler | R Needs | B Bernu | L Tocchio | F Becca | A Parola | S Sorella | S Moroni | M Taddei | M Ruggeri
Source: Journal Club for Condensed Matter Physics (article, 2020-05-31)
Field: Materials Science — Materials Chemistry

Machine learning many-electron wave functions via backflow transformations 1. Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions Authors: D. Luo and B. K. Clark Phys. Rev. Lett. 122, 226401 (2019); DOI:10.1103/PhysRevLett.122.226401 arXiv:1807.10770 2. Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks Authors: D. Pfau, J. S. Spencer, A. G. de G. Matthews, and W. M. C. Foulkes arXiv:1909.02487 3. […] Backflow ; Electron ; Computer science ; Physics ; Engineering ; Quantum mechanics ; Mechanical engineering Machine Learning in Materials Science
Authors: Charly Empereur‐mot | Luca Pesce | Davide Bochicchio | Claudio Perego | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2020-07-07)
Field: Materials Science — Materials Chemistry

Swarm-CG: Automatic Parametrization of Bonded Termsin Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters incoarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatomswithin 4h-24h on standard desktop machines, using an AA trajectory as reference and defaultsettings of the software. The software benefits from a user-friendly interface and two different usagemodes (default and advanced). We particularly expect Swarm-CG to support and facilitate thedevelopment of new CG models for the study of molecular systems interesting for bio- and nanotechnology.Excellent performances are demonstrated using a benchmark of 9 molecules of diversenature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG forcefields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited forthe optimization of models built based also on other CG schemes. Swarm-CG is available with all itsdependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration dataare available at: www.github.com/GMPavanLab/SwarmCG. Swarm behaviour ; Particle swarm optimization ; Computer science ; Metaheuristic ; Software ; Parametrization (atmospheric modeling) ; Multi-swarm optimization ; Python (programming language) ; Swarm intelligence ; Algorithm ; Artificial intelligence ; Physics ; Programming language Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Chemical Synthesis and Analysis
Authors: В. В. Петров | А. А. Kryuchyn | S. O. Kostyukevych | В. М. Рубіш
Source: Реєстрація зберігання і обробка даних (article, 2020-05-25)
Field: Materials Science — Materials Chemistry

Прямий лазерний запис мікрорельєфних структур в халькогенідних склах лазерним променевим записувачем мастер-дисків Methods to create microrelief phase elements for diffractive optics and ophthalmology were analyzed. Direct laser writing, by a laser beam recorder used for master discs is an effective method for the formation of flat microrelief phase elements on thin films of chalcogenide semiconductors, where photo-structural transformations occur under the action of laser radiation. Optics ; Laser ; Chalcogenide ; Materials science ; Holography ; Semiconductor ; Laser beams ; Phase (matter) ; Optoelectronics ; Physics Phase-change materials and chalcogenides ; Optical and Acousto-Optic Technologies ; Optical Polarization and Ellipsometry
Authors: Yushu Hu
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Materials Science — Ceramics and Composites

Decoding the Relationship between Composition and Various Properties of Calcium Silicate and Calcium Aluminosilicate Glasses This dissertation contributes to decode the relationship between glass composition and various properties that are crucial to novel glass design by performing molecular dynamics simulations. The topics include the prediction of glass transition temperature, the origin of glass-forming ability, and the impact of cooling rate on glass relaxation. In general, the first two properties are analyzed by utilizing topological constraint theory. By combining molecular dynamics simulations and topological constraint theory, a fully analytical model is developed to predict the fictive glass transition temperature of (CaO)x(SiO2)1-x glass system. To be specific, this model takes composition as input and provides the prediction of glass transition temperature as output. On the other hand, glass-forming ability is an important factor that guides the manufacturing process while sometimes imposes limitations to glass engineering. Despite many empirical successes to identify and characterize glass-forming ability in various glass systems, there exists a lack of knowledge of physical details in the glass structure. Here, we conduct molecular dynamics simulations of a series of calcium silicate glasses. We show that the flexible-to-rigid topological transition coincides with the compositional window that has optimal glass-forming ability. By explaining this transition from the aspect of internal flexibility and internal stress within the network, we aim to provide an alternative topological explanation for the nature of glass-forming ability. Chapter 3 is a reprint of a previously published journal article. It demonstrates the impact of cooling rate on glass transition by analyzing glass relaxation and hysteresis of a series of silicate glasses. It proves that by extrapolating simulation data, one can access the results that are close to those generated from experimental cooling rates. Glass transition ; Relaxation (psychology) ; Materials science ; Topology (electrical circuits) ; Composite material ; Mathematics Glass properties and applications ; Material Dynamics and Properties