jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-03-03)
Field: Materials Science — Materials Chemistry

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy. Maxima and minima ; Density functional theory ; Ab initio ; Conformational isomerism ; Coupled cluster ; Electronic structure ; Single point ; Point (geometry) ; Cluster (spacecraft) ; Statistical physics ; Physics ; Molecule ; Molecular physics ; Computer science ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Elif GÜLTEKİN | Halil İbrahim Çelık | Serkan Nohut | Suna Karakurd Elma
Source: Journal of the Textile Institute (article, 2020-02-18)
Field: Materials Science — Polymers and Plastics

Predicting air permeability and porosity of nonwovens with image processing and artificial intelligence methods This study was conducted to investigate the relationship between the fiber distribution and the permeability properties of the hydroentangled nonwoven fabrics in terms of air permeability and porosity. The fiber distribution and web properties of the hydroentangled nonwoven fabrics have significant influence on mechanical and physical performance of the finished products. Control of these properties during production, without physical testing, plays an important role in reducing response time to change the process parameters as well as production costs and material waste. In this study, an artificial intelligence method has been developed to predict the porosity and air permeability properties of hydroentangled nonwoven fabrics from their texture features. For this aim, two image processing algorithms were developed in order to measure the fabric porosity and to extract texture statistical features. For the prediction of the investigated fabric properties, an Artificial Neural Network (ANN) model was built. The investigated samples were composed of Polyester (PES) and Viscose (CV) fiber with different areal weights. High regression values were obtained between predicted and actual values for both porosity and air-permeability properties. According to ANN results, it was revealed that the air permeability and porosity properties of hydroentangled nonwovens can be predicted with high accuracy from their texture images. Air permeability specific surface ; Porosity ; Materials science ; Viscose ; Composite material ; Permeability (electromagnetism) ; Polyester ; Nonwoven fabric ; Fiber ; Artificial neural network ; Artificial intelligence ; Computer science Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Surface Roughness and Optical Measurements
Authors: Ryosuke Shibukawa | Shoichi Ishida | Kazuki Yoshizoe | K. Wasa | Kiyosei Takasu | Yasushi Okuno | Kei Terayama | Koji Tsuda
Source: Journal of Cheminformatics (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist's objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called "CompRet" with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists. Enumeration ; Computer science ; Data science ; Data mining ; Information retrieval ; Mathematics Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Stéṕhane Gin | Mengyi Wang | Nicolas Bisbrouck | Mélanie Taron | Xiaonan Lu | Lu Deng | Frédéric Angeli | Thibault Charpentier | Jean‐Marc Delaye | Jincheng Du | Mathieu Bauchy
Source: npj Materials Degradation (article, 2020-03-16)
Field: Materials Science — Ceramics and Composites

Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses? Abstract Tuning glass composition to obtain targeted properties generally relies on empirical approaches. However, a deep understanding of the physical and chemical mechanisms linking glass composition to its structure and properties would enable developing reliable predictive models. Indeed, although empirical models are usually able to interpolate composition–property relationships within a given compositional envelope, they often fail at extrapolating predictions far from their training domain. Here, as an alternative route to empirical models, we show that a structural descriptor based on the number of topological constraints per atom can be used to predict the initial dissolution rate of aluminosilicate and borosilicate glasses after being parameterized on different families of glasses (specific series of borosilicate glasses). Sixteen glasses belonging to these families were studied and their initial dissolution rates were determined at 90 °C and pH 90 °C = 9, covering rates spanning over 5 orders of magnitude. The model based on topological constraints was trained based on seven select borosilicate glasses ( R 2 = 0.997) and used to predict the dissolution rate of nine additional borosilicate and aluminosilicate glasses. We show that, provided that corrections are made for high alkali content glasses that dissolve incongruently (preferential release of Na), the model gives reasonable predictions, even far from its training domain. Borosilicate glass ; Aluminosilicate ; Dissolution ; Materials science ; Mineralogy ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Metallurgy ; Catalysis Glass properties and applications ; Mineralogy and Gemology Studies ; Geological and Geochemical Analysis
Authors: Serge Mignani | Xiangyang Shi | Valentı́n Ceña | Dzmitry Shcharbin | Maria Bryszewska | Jean‐Pierre Majoral
Source: Drug Discovery Today (review, 2020-12-04)
Field: Materials Science — Polymers and Plastics

In vivo therapeutic applications of phosphorus dendrimers: state of the art Dendrimer ; Nanocarriers ; In vivo ; Nanomedicine ; Drug delivery ; Nanotechnology ; Pharmacology ; Chemistry ; Materials science ; Medicine ; Biology ; Biotechnology ; Nanoparticle ; Biochemistry Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Evgeniy M. Chistyakov | Natalya Kolpinskaya | V. F. Posokhova | В. П. Чуев
Source: Polymers (article, 2020-05-20)
Field: Materials Science — Polymers and Plastics

Dental Composition Modified with Aryloxyphosphazene Containing Carboxyl Groups A modifier consisting of the mixture of cyclotriphosphazenes containing 4-allyl-2-methoxyphenoxy and β-carboxyethenylphenoxy moieties was developed for administration with acrylate dental restorative compositions. The synthesized compounds were characterized by 1H and 13C NMR spectroscopy and MALDI-TOF mass spectrometry. The optimal conditions to combine the modifier with the starting dental mixture consisting of bis-GMA and TGM-3 were revealed by differential scanning calorimetry (DSC) method. Properties of the cured modified compositions were evaluated for the compliance with requirements of ISO 4049:2019. It was found that these compositions possess the increased adhesion to dental tissues and cure depth and the decreased water sorption and water solubility. The values of elastic modules, destructive compressive stress and microhardness were also increasing along with the increased content of the modifier in the composition. Differential scanning calorimetry ; Composition (language) ; Materials science ; Solubility ; Polymer chemistry ; Mass spectrometry ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Chromatography Flame retardant materials and properties ; Dental materials and restorations ; Photopolymerization techniques and applications
Authors: Markus Gusenbauer | Harald Oezelt | Johann Fischbacher | Alexander Kovacs | Panpan Zhao | T.G. Woodcock | T. Schrefl
Source: npj Computational Materials (article, 2020-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Extracting local nucleation fields in permanent magnets using machine learning Abstract Microstructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict the macroscopic coercivity from microstructure imaging. Although this is not possible now, in the present work we examine a related question, namely the prediction of simulated nucleation fields of a quasi-three-dimensional (rescaled and extruded) system constructed from a two-dimensional image. We extract features of the image and analyze them via machine learning. A large number of extruded systems are constructed from 10 × 10 pixel sub-images of an Electron Backscatter Diffraction (EBSD) image using an automated meshing procedure. A local nucleation field is calculated by micromagnetic simulation of each quasi-three-dimensional system. Decision trees, trained with the simulation results, can predict nucleation fields of these quasi-three-dimensional systems from new images within seconds. As for now we cannot quantitatively predict the macroscopic coercivity, nevertheless we can identify weak spots in the magnet and see trends in the nucleation field distribution. Coercivity ; Nucleation ; Electron backscatter diffraction ; Magnet ; Materials science ; Field (mathematics) ; Microstructure ; Condensed matter physics ; Diffraction ; Artificial intelligence ; Computer science ; Statistical physics ; Mechanical engineering ; Optics ; Physics ; Mathematics ; Thermodynamics ; Metallurgy ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Jiang Peng | Kun Li | Chen Xi-quan | Ruiqi Dan | Yang Yu
Source: Applied Sciences (article, 2020-02-21)
Field: Materials Science — Surfaces, Coatings and Films

Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil–Water Separation Crude oil spills from offshore oil fields will cause serious pollution to the marine ecological environment. Many 3D porous materials have been used for oil–water separation, but they cannot be widely used due to complex preparation processes and expensive preparation costs. Here, a facile and cheap approach to disperse expanded graphite (EG), stearic acid, and Fe3O4 magnetic nanoparticles on the skeleton surface of polyurethane (PU) sponge to prepare the magnetic and hydrophobic composite polyurethane sponge for oil–water separation. The results show that the composite PU sponge had a strong oil absorption capacity for various oils, the oil adsorption capacities has reached 32–40 g/g, and it has become more hydrophobic. The addition of Fe3O4 magnetic nanoparticles endowed the sponge with magnetic responsivity, and the composite PU sponge still had a strong oil adsorption capacity after several adsorbing-squeezing cycles. The magnetic and hydrophobic composite polyurethane sponge is a very promising material for practical oil adsorption and oil–water separation. Adsorption ; Composite number ; Materials science ; Polyurethane ; Sponge ; Magnetic nanoparticles ; Chemical engineering ; Nanoparticle ; Composite material ; Organic chemistry ; Chemistry ; Nanotechnology Surface Modification and Superhydrophobicity ; Microplastics and Plastic Pollution ; Oil Spill Detection and Mitigation
Authors: Hoang Vu Dang | Huong Truong Thi Thu | Ly Dong Thi Ha | Huong T. M. Nguyen
Source: Journal of Analytical Methods in Chemistry (article, 2020-02-08)
Field: Chemistry — Analytical Chemistry

RP-HPLC and UV Spectrophotometric Analysis of Paracetamol, Ibuprofen, and Caffeine in Solid Pharmaceutical Dosage Forms by Derivative, Fourier, and Wavelet Transforms: A Comparison Study Different signal-transforming algorithms were applied for UV spectrophotometric analysis of paracetamol, ibuprofen, and caffeine in ternary mixtures. Phosphate buffer pH 7.2 was used as the spectrophotometric solvent. Severe overlapping spectra could be resolved into individual bands in the range of wavelengths 200-300 nm by using Savitzky-Golay smoothing and differentiation, trigonometric Fourier series, and mother wavelet functions (i.e., sym6, haar, coif3, and mexh). To optimize spectral recoveries, the concentration of various types of divisors (single, double, and successive) was tested. The developed spectrophotometric methods showed linearity over the ranges 20-40 mg/L for paracetamol, 12-32 mg/L for ibuprofen, and 1-3.5 mg/L for caffeine (<i>R</i> <sup>2</sup> > 0.990). They could be successfully applied to the assay and dissolution test of paracetamol, ibuprofen, and caffeine in their combined tablets and capsules, with accuracy (99.1-101.5% recovery) and precision (RSD < 2%). For comparison, an isocratic RP-HPLC analysis was also developed and validated on an Agilent ZORBAX Eclipse XDB-C18 (150 × 4.6 mm, 5 <i>µ</i>m) at an ambient temperature. A mixture of methanol : phosphate buffer 0.01 M pH 3 (30 : 70 v/v) was used as the mobile phase delivered at 2 mL/min, and the effluent was monitored at 225 nm. It was shown that spectrophotometric data were statistically comparable to HPLC (<i>p</i> > 0.05), suggesting possible interchange between UV spectrophotometric and HPLC methods for routine analysis of paracetamol, ibuprofen, and caffeine in their solid pharmaceutical dosage forms. Chromatography ; Chemistry ; High-performance liquid chromatography ; Ibuprofen ; Caffeine ; Spectrophotometry ; Derivative (finance) ; Dosage form ; Analytical Chemistry (journal) Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Nobuaki Kono | Hiroyuki Nakamura | Masaru Mori | Masaru Tomita | Kazuharu Arakawa
Source: Scientific Reports (article, 2020-09-24)
Field: Materials Science — Biomaterials

Spidroin profiling of cribellate spiders provides insight into the evolution of spider prey capture strategies Orb-weaving spiders have two main methods of prey capture: cribellate spiders use dry, sticky capture threads, and ecribellate spiders use viscid glue droplets. Predation behaviour is a major evolutionary driving force, and it is important on spider phylogeny whether the cribellate and ecribellate spiders each evolved the orb architecture independently or both strategies were derived from an ancient orb web. These hypotheses have been discussed based on behavioural and morphological characteristics, with little discussion on this subject from the perspective of molecular materials of orb web, since there is little information about cribellate spider-associated spidroin genes. Here, we present in detail a spidroin catalogue of six uloborid species of cribellate orb-weaving spiders, including cribellate and pseudoflagelliform spidroins, with transcriptome assembly complemented with long read sequencing, where silk composition is confirmed by proteomics. Comparative analysis across families (Araneidae and Uloboridae) shows that the gene architecture, repetitive domains, and amino acid frequencies of the orb web constituting silk proteins are similar among orb-weaving spiders regardless of the prey capture strategy. Notably, the fact that there is a difference only in the prey capture thread proteins strongly supports the monophyletic origin of the orb web. Spider ; Orb (optics) ; Predation ; Biology ; Creatures ; Weaving ; Evolutionary biology ; Spider silk ; Theridiidae ; Ecology ; Zoology ; Computer science ; Artificial intelligence ; Paleontology Silk-based biomaterials and applications ; Spider Taxonomy and Behavior Studies ; Genomics and Phylogenetic Studies
Authors: Matthias Fey | Jan-Gin Yuen | Frank Weichert
Source: arXiv (Cornell University) (preprint, 2020-06-22)
Field: Materials Science — Materials Chemistry

Hierarchical Inter-Message Passing for Learning on Molecular Graphs We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph representation and its associated junction tree, where nodes represent meaningful clusters in the original graph, e.g., rings or bridged compounds. We then proceed to learn a molecule's representation by passing messages inside each graph, and exchange messages between the two representations using a coarse-to-fine and fine-to-coarse information flow. Our method is able to overcome some of the restrictions known from classical GNNs, like detecting cycles, while still being very efficient to train. We validate its performance on the ZINC dataset and datasets stemming from the MoleculeNet benchmark collection. Computer science ; Message passing ; Graph ; Theoretical computer science ; Benchmark (surveying) ; Representation (politics) ; Tree (set theory) ; Artificial intelligence ; Parallel computing ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Dorota Chudoba | Katarzyna Łudzik | Monika Jażdżewska | S. Wołoszczuk
Source: International Journal of Molecular Sciences (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

Kinetic and Equilibrium Studies of Doxorubicin Adsorption onto Carbon Nanotubes This study provides deep insight into the adsorption process of doxorubicin onto different types of carbon nanotubes that have been proved to show attractive properties as a drug delivery system. The main aim of the work was to propose probable adsorption mechanisms and interactions between the anticancer drug and surface of modified and pristine carbon nanotubes at blood pH. The carbon nanotubes were oxidized to optimize the absorbance efficiency relative to that of pristine multiwalled carbon nanotubes. The adsorption isotherm of the modified system was well described by the Temkin equation. It confirms that the adsorption in the system studied involves also hydrogen and covalent bonding and is exothermic in nature. The experimental kinetic curves of adsorption were fitted to different mathematical models to check if the kinetics of doxorubicin adsorption onto the modified multiwalled carbon nanotubes follows a pseudo-second-order model and the chemical sorption is bound to be the rate-limiting. On the basis of the molecular dynamics simulation, it was shown that <i>in vacuo</i> the aggregation tendency of doxorubicin molecules is far more favorable than their adsorption on pristine carbon nanotubes (CNTs). It suggests that only functionalization of the nanotube surface can affect the interaction between doxorubicin and functional groups of the carriers and increases the efficiency of the drug loading process. Adsorption ; Carbon nanotube ; Chemical engineering ; Materials science ; Sorption ; Chemistry ; Organic chemistry ; Nanotechnology Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Jonathan K. Challis | Xavier Ortiz Almirall | Paul A. Helm | Charles S. Wong
Source: Environmental Pollution (article, 2020-01-29)
Field: Chemistry — Analytical Chemistry

Performance of the organic-diffusive gradients in thin-films passive sampler for measurement of target and suspect wastewater contaminants Sampling (signal processing) ; Contamination ; Environmental chemistry ; Chemistry ; Wastewater ; Environmental science ; Diffusive gradients in thin films ; Soil science ; Environmental engineering ; Computer science Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Metabolomics and Mass Spectrometry Studies
Authors: Achraf Harrou | Elkhadir Gharibi | Hicham Nasri | Meriam El Ouahabi
Source: SN Applied Sciences (article, 2020-01-27)
Field: Materials Science — Biomaterials

Thermodynamics and kinetics of the removal of methylene blue from aqueous solution by raw kaolin Kaolinite ; Halloysite ; Adsorption ; Sorption ; Illite ; Endothermic process ; Aqueous solution ; Chemistry ; Methylene blue ; Kinetics ; Clay minerals ; Chemical engineering ; Exothermic process ; Organic chemistry ; Mineralogy ; Catalysis Clay minerals and soil interactions ; Adsorption and biosorption for pollutant removal ; Soil and Unsaturated Flow
Authors: Luis Castillo Henríquez | Rolando Vargas-Zúñiga | Jorge Pacheco-Molina | José Roberto Vega‐Baudrit
Source: ADMET & DMPK (review, 2020-07-05)
Field: Materials Science — Biomaterials

Electrospun nanofibers: A nanotechnological approach for drug delivery and dissolution optimization in poorly water-soluble drugs Electrospinning is a novel and sophisticated technique for the production of nanofibers with high surface area, extreme porous structure, small pore size, and surface morphologies that make them suitable for biomedical and bioengineering applications, which can provide solutions to current drug delivery issues of poorly water-soluble drugs. Electrospun nanofibers can be obtained through different methods asides from the conventional one, such as coaxial, multi-jet, side by side, emulsion, and melt electrospinning. In general, the application of an electric potential to a polymer solution causes a charged liquid jet that moves downfield to an oppositely charged collector, where the nanofibers are deposited. Plenty of polymers that differ in their origin, degradation character and water affinity are used during the process. Physicochemical properties of the drug, polymer(s), and solvent systems need to be addressed to guarantee successful manufacturing. Therefore, this review summarizes the recent progress in electrospun nanofibers for their use as a nanotechnological tool for dissolution optimization and drug delivery systems for poorly water-soluble drugs. Nanofiber ; Electrospinning ; Materials science ; Drug delivery ; Emulsion ; Polymer ; Nanotechnology ; Dissolution ; Chemical engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Sensor and Energy Harvesting Materials