jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. Balasubramanian
Source: Symmetry (article, 2021-10-02)
Field: Materials Science — Materials Chemistry

Symmetry, Combinatorics, Artificial Intelligence, Music and Spectroscopy Symmetry forms the foundation of combinatorial theories and algorithms of enumeration such as Möbius inversion, Euler totient functions, and the celebrated Pólya’s theory of enumeration under the symmetric group action. As machine learning and artificial intelligence techniques play increasingly important roles in the machine perception of music to image processing that are central to many disciplines, combinatorics, graph theory, and symmetry act as powerful bridges to the developments of algorithms for such varied applications. In this review, we bring together the confluence of music theory and spectroscopy as two primary disciplines to outline several interconnections of combinatorial and symmetry techniques in the development of algorithms for machine generation of musical patterns of the east and west and a variety of spectroscopic signatures of molecules. Combinatorial techniques in conjunction with group theory can be harnessed to generate the musical scales, intensity patterns in ESR spectra, multiple quantum NMR spectra, nuclear spin statistics of both fermions and bosons, colorings of hyperplanes of hypercubes, enumeration of chiral isomers, and vibrational modes of complex systems including supergiant fullerenes, as exemplified by our work on the golden fullerene C150,000. Combinatorial techniques are shown to yield algorithms for the enumeration and construction of musical chords and scales called ragas in music theory, as we exemplify by the machine construction of ragas and machine perception of musical patterns. We also outline the applications of Hadamard matrices and magic squares in the development of algorithms for the generation of balanced-pitch chords. Machine perception of musical, spectroscopic, and symmetry patterns are considered. Artificial intelligence ; Mathematics ; Computer science ; Combinatorics Machine Learning in Materials Science ; History and advancements in chemistry ; Molecular spectroscopy and chirality
Authors: Mikołaj Sacha | Mikołaj Błaż | Piotr Byrski | Paweł Włodarczyk-Pruszyński | Mikołaj Chromiński | Rafał Loska | Stanisław Jastrzębski
Source: Journal of Chemical Information and Modeling (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large data sets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https://github.com/molecule-one/megan. Retrosynthetic analysis ; Template ; Computer science ; Graph ; Theoretical computer science ; Chemical space ; Graph rewriting ; Artificial intelligence ; Programming language ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Genomics and Phylogenetic Studies
Authors: S. Rajeshkumar | Rajaduraipandian Subramaniam | Sabeena Gabrial Kathirason | Daoud Ali | Sri Renukadevi Balusamy | G. Annadurai | Kalirajan Arunachalam | H. Sellami
Source: BioMed Research International (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

[Retracted] Curcumin‐Chitosan Nanocomposite Formulation Containing Pongamia pinnata‐Mediated Silver Nanoparticles, Wound Pathogen Control, and Anti‐Inflammatory Potential This advancement in the field of biomaterials, which means nanocomposite, not only helps to reduce the harmful effects of pathogenic organisms while representing an environmentally benign material but it also shows to be a material with zero danger to humans and the environment. Curcumin ; Nanocomposite ; Chitosan ; Antimicrobial ; Pongamia ; Silver nanoparticle ; Materials science ; Nanomedicine ; Nanoparticle ; Antibacterial activity ; Nanotechnology ; Chemistry ; Medicine ; Pharmacology ; Biology ; Bacteria ; Organic chemistry Nanoparticles: synthesis and applications ; Curcumin's Biomedical Applications ; Synthesis of Tetrazole Derivatives
Authors: Karolina Górnicka | Gabriel Kuderowicz | Michał J. Winiarski | Bartłomiej Wiendlocha | Tomasz Klimczuk
Source: Scientific Reports (article, 2021-08-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Superconductivity in LiGa2Ir Heusler type compound with VEC = 16 Polycrystalline LiGa<sub>2</sub>Ir has been prepared by a solid state reaction method. A Rietveld refinement of powder x-ray diffraction data confirms a previously reported Heusler-type crystal structure (space group Fm-3m, No. 225) with lattice parameter a = 6.0322(1) Å. The normal and superconducting state properties were studied by magnetic susceptibility, heat capacity, and electrical resistivity techniques. A bulk superconductivity with T<sub>c</sub> = 2.94 K was confirmed by detailed heat capacity studies. The measurements indicate that LiGa<sub>2</sub>Ir is a weak-coupling type-II superconductor ([Formula: see text]<sub>e-p</sub> = 0.57, [Formula: see text]C/[Formula: see text]T<sub>c</sub> = 1.4). Electronic structure, lattice dynamics, and the electron-phonon interaction are studied from first principles calculations. Ir and two Ga atoms equally contribute to the Fermi surface with a minor contribution from Li. The phonon spectrum contains separated high frequency Li modes, which are seen clearly as an Einstein-like contribution in the specific heat. The calculated electron-phonon coupling constant [Formula: see text]<sub>e-p</sub> = 0.68 confirms the electron-phonon mechanism for the superconductivity. LiGa<sub>2</sub>Ir and recently reported isoelectronic LiGa<sub>2</sub>Rh are the only two known representatives of the Heusler superconductors with the valence electron count VEC = 16. Superconductivity ; Type (biology) ; Materials science ; Condensed matter physics ; Physics ; Biology Heusler alloys: electronic and magnetic properties ; Rare-earth and actinide compounds ; MXene and MAX Phase Materials
Authors: Andra Dinache | Mihail–Lucian Pascu | Adriana Smarandache
Source: Molecules (review, 2021-12-20)
Field: Materials Science — Materials Chemistry

Spectral Properties of Foams and Emulsions The optical and spectral properties of foams and emulsions provide information about their micro-/nanostructures, chemical and time stability and molecular data of their components. Foams and emulsions are collections of different kinds of bubbles or drops with particular properties. A summary of various surfactant and emulsifier types is performed here, as well as an overview of methods for producing foams and emulsions. Absorption, reflectance, and vibrational spectroscopy (Fourier Transform Infrared spectroscopy-FTIR, Raman spectroscopy) studies are detailed in connection with the spectral characterization techniques of colloidal systems. Diffusing Wave Spectroscopy (DWS) data for foams and emulsions are likewise introduced. The utility of spectroscopic approaches has grown as processing power and analysis capabilities have improved. In addition, lasers offer advantages due to the specific properties of the emitted beams which allow focusing on very small volumes and enable accurate, fast, and high spatial resolution sample characterization. Emulsions and foams provide exceptional sensitive bases for measuring low concentrations of molecules down to the level of traces using spectroscopy techniques, thus opening new horizons in microfluidics. Characterization (materials science) ; Spectroscopy ; Materials science ; Raman spectroscopy ; Fourier transform infrared spectroscopy ; Microfluidics ; Absorption spectroscopy ; Absorption (acoustics) ; Infrared spectroscopy ; Nanotechnology ; Fourier transform spectroscopy ; Optics ; Chemistry ; Composite material ; Organic chemistry ; Physics Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Surfactants and Colloidal Systems
Authors: Kaory Barrientos | María Isabel Gaviria | Juan Pablo Arango | Jersson Plácido | Sandra Bustamante | Martha Elena Londoño | Marisol Jaramillo
Source: Processes (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

Synthesis, Characterization and Ecotoxicity Evaluation of Biochar-Derived Carbon Dots from Spruce Tree, Purple Moor-Grass and African Oil Palm Biochar-derived C-Dots from Picea, Molinia caerulea and Elaeis guineensis were synthesized through a hydrothermal process, and their physicochemical and optical characteristics and environmental effects were compared. These C-Dots were characterized by techniques such as Attenuated Total Reflection–Fourier Transform Infrared (ATR-FTIR), UV-Vis spectrophotometry, fluorescence spectroscopy, dynamic light scattering (DLS), Z potential, and High-Resolution Transmission Electronical Microscopy (HR-TEM). The ecotoxicity tests were performed using the Microtox™ test, making this study one of the few that use this method. The C-Dots from Molinia caerulea showed the best quantum yield (QY) of 8.39% and moderate ecotoxicity, while Elaeis guineensis has the lowest QY (2.31%) but with zero toxicity. Furthermore, the C-Dots from Picea presents good optical properties but showed high toxicity and limits its use. Finally, all C-Dots showed functional groups that could be biofunctionalized with biomolecules, especially C-Dots from Molinia caerulea and Elaeis guineensis show potential for use in the development of optical biosensors. Ecotoxicity ; Elaeis guineensis ; Biochar ; Chemistry ; Materials science ; Palm oil ; Food science ; Toxicity ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Soo-In Sohn | Subramani Pandian | John‐Lewis Zinia Zaukuu | Young‐Ju Oh | Soo-Yun Park | Chae Sun Na | Eun-Kyoung Shin | Hyeon‐Jung Kang | Tae‐Hun Ryu | Woosuk Cho | Youn-Sung Cho
Source: International Journal of Molecular Sciences (article, 2021-12-25)
Field: Chemistry — Analytical Chemistry

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM <i>Brassica napus, B. rapa</i>, and F1 hybrids (<i>B. rapa</i> X GM <i>B. napus</i>). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM <i>B. napus, B. rapa</i>, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination. Brassica rapa ; Brassica ; Canola ; Biology ; Hybrid ; Partial least squares regression ; Biotechnology ; Agronomy ; Machine learning ; Computer science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Listeria monocytogenes in Food Safety
Authors: Soumyashree S. Panda | Ravi S. Hegde
Source: Nanophotonics (article, 2021-12-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A learning based approach for designing extended unit cell metagratings The possibility of arbitrary spatial control of incident wavefronts with the subwavelength resolution has driven research into dielectric optical metasurfaces in the last decade. The unit-cell based metasurface design approach that relies on a library of single element responses is known to result in reduced efficiency attributed to the inadequate accounting of the coupling effects between meta-atoms. Metasurfaces with extended unit-cells containing multiple resonators can improve design outcomes but their design requires extensive numerical computing and optimizations. We report a deep learning based design methodology for the inverse design of extended unit-cell metagratings. In contrast to previous reports, our approach learns the metagrating spectral response across its reflected and transmitted orders. Through systematic exploration, we discover network architectures and training dataset sampling strategies that allow such learning without requiring extensive ground-truth generation. The one-time investment of model creation can then be used to significantly accelerate numerical optimization of multiple functionalities as demonstrated by considering the inverse design of various spectral and polarization dependent splitters and filters. The proposed methodology is not limited to these proof-of-concept demonstrations and can be broadly applied to meta-atom-based nanophotonic system design and in realising the next generation of metasurface functionalities with improved performance. Computer science ; Inverse ; Wavefront ; Electronic engineering ; Computer engineering ; Computer architecture ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Advanced Antenna and Metasurface Technologies
Authors: Aristotelis P. Sgouros | Stefan Knippenberg | Maxime Guillaume | Doros N. Theodorou
Source: Soft Matter (article, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Multiscale simulations of polyzwitterions in aqueous bulk solutions and brush array configurations Zwitterionic polymers are very promising candidates for antifouling materials that exhibit high chemical stability as compared to polyethylene glycol-based systems. A number of simulation and experimental studies have emerged over recent years for the investigation of sulfobetaine-based zwitterionic polymers. Investigating the structural and thermodynamic properties of such polymers requires access to broad time and length regimes, thus necessitating the development of multiscale simulation strategies. The present article advocates a mesoscopic dissipative particle dynamics (DPD) model capable of addressing a wide range of time and length scales. The mesoscopic force field was developed hand-in-hand with atomistic simulations based on the OPLS force field through a bottom-up parameterization procedure that matches the atomistically calculated strand-length, strand-angle and pair distribution functions. The DPD model is validated against atomistic simulations conducted in this work, and against relevant atomistic simulation studies, theoretical predictions and experimental correlations from the literature. Properties examined include the conformations of SPE polymers in dilute bulk aqueous solution, the density profile and thickness of brush arrays as functions of the grafting density and chain length. In addition, we compute the potential of mean force of an approaching hydrophilic or hydrophobic foulant <i>via</i> umbrella sampling as a function of its position relative to the poly-zwitterion-covered surface. The aforementioned observables lead to important insights regarding the conformational tendencies of grafted polyzwitterions and their antifouling properties. Dissipative particle dynamics ; Mesoscopic physics ; Molecular dynamics ; Zwitterion ; Polymer ; Force field (fiction) ; Materials science ; Potential of mean force ; Chemical physics ; Aqueous solution ; Multiscale modeling ; Statistical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Physical chemistry ; Molecule ; Composite material Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Dendrimers and Hyperbranched Polymers
Authors: T. Pooventhiran | Ephraim Felix Marondedze | Penny P. Govender | Utsab Bhattacharyya | D. Jagadeeswara Rao | Elham S. Aazam | Jinesh M. Kuthanapillil | Tomlal Jose E | Renjith Thomas
Source: Journal of Molecular Modeling (article, 2021-09-04)
Field: Chemistry — Organic Chemistry

Energy and reactivity profile and proton affinity analysis of rimegepant with special reference to its potential activity against SARS-CoV-2 virus proteins using molecular dynamics Molecular dynamics ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Chemistry ; Coronavirus disease 2019 (COVID-19) ; Reactivity (psychology) ; Virology ; Biophysics ; Computational chemistry ; Biology ; Medicine ; Infectious disease (medical specialty) Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; thermodynamics and calorimetric analyses
Authors: Tao Zhang | Dongxin He | Junyu Wei | Hongshun Liu | Qingquan Li | G. Teyssèdre
Source: High Voltage (article, 2021-10-05)
Field: Materials Science — Materials Chemistry

Charge vibration behaviour in polyimide under the pulse voltage with different rise and fall times Abstract High‐voltage electronic equipment is widely used in power systems, which normally operate under the pulse voltage condition. However, compared to DC or AC stress, the electrical insulation suffers a more severe ageing process under a pulse voltage with steep edges and so may cause electrical equipments to fail early. The charge behaviour in polyimide under the pulse voltage with different edge times was investigated in this study. The experimental results show that charges vibrate at the pulse edge, while the amplitude of charge vibration increases with decreasing edge times. Furthermore, the detected vibration signal is strong within the nanosecond edge times, while being negligible in the microsecond range. Notably, the amplitude of charge vibration at the falling edge is larger than that at the rising edge in the presence of the column‐plate electrode. It is believed that the resultant force acting on the charge increases drastically at the pulse edges, leading to the charge movement and the generation of ultrasonic waves, with the resultant force becoming larger when the edge times decrease. Charge vibration behaviour is harmful to the microstructure of insulation materials, and so it may be concluded that the rise and fall times of the pulse voltage play an important role in insulation failure. Polyimide ; Rise time ; Charge (physics) ; Voltage ; Pulse (music) ; Vibration ; Materials science ; Electrical engineering ; Physics ; Composite material ; Optoelectronics ; Acoustics ; Engineering ; Layer (electronics) ; Quantum mechanics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electrostatic Discharge in Electronics
Authors: Muhammad Yunus Firdaus | Herlinda Octaviani | Humaira Herlini | Nurul Fatimah | Tika Mulyaningsih | Zachnaz Fairuuz | Asep Bayu Dani Nandiyanto
Source: Mediterranean Journal of Chemistry (article, 2021-01-07)
Field: Materials Science — Polymers and Plastics

Review: The Comparison of Clay Modifier (Cloisite Types) in Various Epoxy-Clay Nanocomposite Synthesis Methods Nanocomposites are a new material discovery in the 21st century. One of the nanocomposite materials which are useful in life is epoxy-clay nanocomposites. Epoxy clay nanocomposites have a reasonably wide application in industrial fields such as aerospace, defense, automobile, etc. The purpose of writing this review is to conduct a literature review on mechanical properties in various Cloisite as a filler of Epoxy-clay Nanocomposites. There are several examples of cloisite, namely Cloisite 10A, Cloisite 15A, Cloisite 20A, Cloisite 25A, Cloisite 30B, and Cloisite 93A. Cloisite has the advantage of producing mechanical properties, especially in the tensile modulus and strength, which is more increased than conventional reinforcing materials. These methods' synthesis results were then characterized using TEM, SEM, XRD, and other tests to determine their mechanical properties. The material parameters resulting from nanocomposites' synthesis are well seen from the high Tensile strength and modulus values. The highest increase in mechanical properties was found in the cloisite 93A by the ultrasonic synthesis method or mechanical stirring based on the study results. Nanocomposite ; Epoxy ; Ultimate tensile strength ; Composite material ; Materials science ; Modulus ; Tensile testing ; Elongation Polymer Nanocomposites and Properties ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Mikhail Elyashberg | Antony Williams
Source: Molecules (review, 2021-11-01)
Field: Chemistry — Spectroscopy

ACD/Structure Elucidator: 20 Years in the History of Development The first methods associated with the Computer-Assisted Structure Elucidation (CASE) of small molecules were published over fifty years ago when spectroscopy and computer science were both in their infancy. The incredible leaps in both areas of technology could not have been envisaged at that time, but both have enabled CASE expert systems to achieve performance levels that in their present state can outperform many scientists in terms of speed to solution. The computer-assisted analysis of enormous matrices of data exemplified 1D and 2D high-resolution NMR spectroscopy datasets can easily solve what just a few years ago would have been deemed to be complex structures. While <i>not</i> a panacea, the application of such tools can provide support to even the most skilled spectroscopist. By this point the structures of a great number of molecular skeletons, including hundreds of complex natural products, have been elucidated using such programs. At this juncture, the expert system ACD/Structure Elucidator is likely the most advanced CASE system available and, being a commercial software product, is installed and used in many organizations. This article will provide an overview of the research and development required to pursue the lofty goals set almost two decades ago to facilitate highly automated approaches to solving complex structures from analytical spectroscopy data, using NMR as the primary data-type. LEAPS ; Computer science ; Data science ; Set (abstract data type) ; Panacea (medicine) ; Expert system ; Artificial intelligence Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Protein Structure and Dynamics