jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew M. Teale | Trygve Helgaker | Andreas Savin | Carlo Adamo | Bálint Aradi | Alexei V. Arbuznikov | Paul Ayers | Evert Jan Baerends | Vincenzo Barone | Patrizia Calaminici | Éric Cancès | Emily A. Carter | Pratim Kumar Chattaraj | Henry Chermette | Ilaria Ciofini | Daniel A. Crawford | Frank De Proft | John F. Dobson | Caludia Draxl | Thomas Frauenheim | Emmanuel Fromager | Patricio Fuentealba | Laura Gagliardi | Giulia Galli | Jiali Gao | Paul Geerlings | Nikitas Gidopoulous | Peter M. W. Gill | Paola Gori‐Giorgi | Andreas Görling | Tim Gould | Stefan Grimme | O. V. Gritsenko | Hans Jørgen Aa. Jensen | Erin R. Johnson | R. Jones | Martin Kaupp | Andreas M. Köster | Leeor Kronik | Anna I. Krylov | Simen Kvaal | Andre Laestadius | Mel Levy | Mathieu Lewin | Shubin Liu | Pierre‐François Loos | Neepa T. Maitra | Frank Neese | John P. Perdew | Katarzyna Pernal | Pascal Pernot | Piotr Piecuch | Elisa Rebolini | Lucia Reining | Pina Romaniello | Adrienn Ruzsinszky | Dennis R. Salahub | Matthias Scheffler | Peter Schwerdtfeger | Viktor N. Staroverov | Jianwei Sun | Erik I. Tellgren | David J. Tozer | Sam Trickey | Carsten A. Ullrich | Alberto Vela | Giovanni Vignale | Tomasz A. Wesołowski | Xin Xu | Weitao Yang
Source: ChemRxiv (preprint, 2022-08-10)
Field: Materials Science — Materials Chemistry

DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 300 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 776 entries, the paper represents a broad snapshot of DFT, anno 2022. Snapshot (computer storage) ; Density functional theory ; Computer science ; Field (mathematics) ; Quantum chemistry ; Chemistry ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Materials science ; Mathematics ; Database ; Pure mathematics Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Roberto Rozas | Vladimir Ankudinov | P. K. Galenko
Source: Journal of Physics Condensed Matter (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Kinetics of rapid growth and melting of Al<sub>50</sub>Ni<sub>50</sub> alloying crystals: phase field theory versus atomistic simulations revisited <sup>*</sup> A revised study of the growth and melting of crystals in congruently melting Al<sub>50</sub>Ni<sub>50</sub>alloy is carried out by molecular dynamics (MDs) and phase field (PF) methods. An embedded atom method (EAM) potential of Purja Pun and Mishin (2009<i>Phil. Mag.</i>89 3245) is used to estimate the material's properties (density, enthalpy, and self-diffusion) of the B2 crystalline and liquid phases of the alloy. Using the same EAM potential, the melting temperature, density, and diffusion coefficient become well comparable with experimental data in contrast with previous works where other potentials were used. In the new revision of MD data, the kinetics of melting and solidification are quantitatively evaluated by the 'crystal-liquid interface velocity-undercooling' relationship exhibiting the well-known bell-shaped kinetic curve. The traveling wave solution of the kinetic PF model as well as the hodograph equation of the solid-liquid interface quantitatively describe the 'velocity-undercooling' relationship obtained in the MD simulation in the whole range of investigated temperatures for melting and growth of Al<sub>50</sub>Ni<sub>50</sub>crystals. Supercooling ; Thermodynamics ; Diffusion ; Molecular dynamics ; Phase (matter) ; Entropy of fusion ; Kinetic energy ; Materials science ; Embedded atom model ; Congruent melting ; Enthalpy ; Atmospheric temperature range ; Phase diagram ; Enthalpy of fusion ; Chemistry ; Differential scanning calorimetry ; Computational chemistry Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Yuyin Zhang | Hongtao Guo | Shaohua Jiang | Zhaoyu Hu | Guojun Zha | Kunming Liu | Haoqing Hou
Source: e-Polymers (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Synthesis and properties of PI composite films using carbon quantum dots as fillers Abstract Polyimide (PI) is widely used in the field of microelectronics because of its excellent thermal, mechanical, optical, and electrical properties. With the development of electronics and information industry, PI as a dielectric material needs to possess low dielectric loss. PI/carbon quantum dots (PI/CQDs) composite films with low dielectric loss were prepared by introducing CQDs into PI matrix. At 25°C and 1 kHz voltage, the dielectric loss of pure PI film is about 0.0057. The dielectric loss of PI/CQDs composite film is about 0.0018, which is about 68% lower than that of pure PI film. The dielectric loss of PI/CQD composite film is greatly reduced while the mechanical properties and thermal properties of PI/CQDs composite film roughly remain unchanged. Due to the cross-linking structure formed between CQDs and PI molecular chain, the relative movement of PI molecular chain is hindered. Polyimide ; Dielectric ; Composite number ; Materials science ; Microelectronics ; Dielectric loss ; Composite material ; Pi ; Optoelectronics ; Chemistry ; Layer (electronics) Synthesis and properties of polymers ; Conducting polymers and applications ; Polydiacetylene-based materials and applications
Authors: Xuemei Ge | Shang Wu | Wen Shen | Lijuan Chen | Yan Zheng | Fen Ao | Yuanlan Ning | Yueyang Mao | Zhong Chen
Source: Bioengineering (article, 2022-03-23)
Field: Materials Science — Biomaterials

Preparation of Polyvinylidene Fluoride–Gold Nanoparticles Electrospinning Nanofiber Membranes In this work, gold nanoparticles (AuNPs) and curcumin drug were incorporated in polyvinylidene fluoride (PVDF) nanofibers by electrospinning as a novel tissue engineering scaffold in nerve regeneration. The influence of AuNPs on the morphology, crystallinity, and drug release behavior of nanofiber membranes was characterized. A successful composite nanofiber membrane sample was observed by scanning electron microscopy (SEM). The addition of AuNPs showed the improved as well as prolonged cumulative release of the drug. The results indicated that PVDF-AuNPs nanofiber membrane could potentially be applied for nerve regeneration. Polyvinylidene fluoride ; Electrospinning ; Nanofiber ; Materials science ; Membrane ; Colloidal gold ; Scanning electron microscope ; Crystallinity ; Chemical engineering ; Nanoparticle ; Polymer chemistry ; Nanotechnology ; Composite material ; Chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Nerve injury and regeneration ; Conducting polymers and applications
Authors: Shipeng Dong | Zihao Wu | Mingjie Wang | Xiaoyan Sun | Liang Mao
Source: SmartMat (article, 2022-11-07)
Field: Materials Science — Materials Chemistry

Assessing comparable bioconcentration potentials for nanoparticles in aquatic organisms via combined utilization of machine learning and toxicokinetic models Abstract The toxicokinetic (TK) model‐derived kinetic bioconcentration factor (BCF k ) provides a quantitatively comparable index to estimate the bioaccumulation potential of nanoparticles (NPs) that barely reach thermodynamic equilibrium in aquatic organisms, but experimental data are limited for various NPs. In the present study, a machine learning model was applied to offer reliable in silico predictions for the dynamic body burden of diverse NPs to derive corresponding parameters for the TK model. The developed eXtreme Gradient Boosting‐derived TK (XGB‐TK) model was applied to predict BCF k results for a broad range of metallic or carbonaceous NPs, with an appreciable prediction R 2 of 0.96. The BCF k values were predicted based on a random combination of selected variable features, revealing that their bioaccumulation potential showed an overall negative correlation with NP density or organism size. By applying importance analysis and partial dependence plots, NP density and organism size were revealed to be the top essential features that impact the bioaccumulation potential. The conjunctively used XGB‐TK model enabled a prior comparison for diverse NPs and straightforward derivation on the dependency of features, which could also guide the bioaccumulation mechanism exploration and experimental condition formulation. Bioconcentration ; Bioaccumulation ; Organism ; In silico ; Biological system ; Environmental chemistry ; Chemistry ; Toxicokinetics ; Biochemical engineering ; Biology ; Toxicity Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology
Authors: Bhavesh Namdev | Senthil Venkatachalam | N Jawahar | Anushma Chorsiya
Source: Indian Journal of Pharmaceutical Education and Research (review, 2022-03-16)
Field: Chemistry — Spectroscopy

A Brief Review on Solubility Enhancement Technique: Hydrotropy In this review we have elaborated about number of hypothesis postulated to understand the mechanism of hydrotropic agents, although majority of the postulated hypothesis haven't justified the solubility enhancement mechanism. Further to it the articles focuses upon the parameters affecting enhancement of solubilizing ability. Hydrotropes are extensively used in drug solubilization, extraction agents for various phytoconstituents, separation agent used for pharmaceutical analysis, as well as for increasing the rate and yield of a chemical reaction due to their unique features. Because of this, hydrotropic solutions possess high industrial demand. While mixed hydrotropy is mostly used in enhancement of solubility, extraction of phytoconstituents and separation by the use of more than one hydrotropic agent. This review article also highlights the applications and future prospects with the employment of hydrotropes and hydrotropic agents in novel drug delivery or parental or oral or tropical systems to enhance solubility profile the drugsare also discussed. Solubility ; Solubilization ; Extraction (chemistry) ; Bioavailability ; Chemistry ; Yield (engineering) ; Biochemical engineering ; Drug ; Combinatorial chemistry ; Chromatography ; Organic chemistry ; Materials science ; Pharmacology ; Medicine ; Biochemistry ; Engineering Analytical Chemistry and Chromatography ; Drug Solubulity and Delivery Systems ; Surfactants and Colloidal Systems
Authors: I. A. Malbakhova | A. S. Bagishev | Alexander Vorobyev | Tatiana Borisenko | O. A. Logutenkо | A. I. Titkov
Source: Ceramics (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Fabrication of NiO/YSZ-Based Anodes for Solid Oxide Fuel Cells by Hybrid 3D Inkjet Printing and Laser Treatment An anode for solid oxide fuel cells (SOFCs) was fabricated using 3D inkjet printing and layer-by-layer laser treatment of compositions based on the NiO/YSZ system followed by thermal sintering. The samples were characterized by scanning electron microscopy and X-ray phase analysis. The study of the morphology of the as-prepared samples revealed the presence of both interlayer macroporosity and intralayer microporosity, which depends on the laser exposure during laser treatment. The use of graphite directly added to the printing composition as the pore former increased the intralayer porosity. The morphology and size of the pores were shown to be similar to those of the pore former. The microstructure and porosity of the anode support can be controlled by varying the laser exposure values and the graphite content of the ceramic composite, which in turn opens up great prospects for using these paste compositions and printing techniques for the manufacture of SOFC anode supports. Materials science ; Anode ; Yttria-stabilized zirconia ; Graphite ; Microstructure ; Non-blocking I/O ; Porosity ; Ceramic ; Layer (electronics) ; Sintering ; Fabrication ; Screen printing ; Scanning electron microscope ; Solid oxide fuel cell ; Oxide ; Composite material ; Selective laser sintering ; Chemical engineering ; Metallurgy ; Cubic zirconia ; Electrode Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: David Mareček | Julian Oberreiter | Andrew Nelson | Stefan Kowarik
Source: Journal of Applied Crystallography (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector q , as is commonly done, but as a function of both q and time. The real-space structures extracted from the XRR curves are restricted to be solutions of a physics-informed growth model and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves R ( q , t ) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves, even if they are sparsely sampled, with a sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times. The approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with less beam damage. X-ray reflectivity ; Reciprocal lattice ; Physics ; Optics ; Computer science ; Kinetic energy ; Algorithm ; Computational physics ; Materials science ; Reflectivity Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Model Reduction and Neural Networks
Authors: Geraudys Mora-Barzaga | Felipe J. Valencia | Matías I. Carrasco | Rafael I. González | Martín G. Parlanti | E. N. Miranda | Eduardo M. Bringa
Source: Nanomaterials (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

Enhancing the Thermal Conductivity of Amorphous Carbon with Nanowires and Nanotubes The thermal conductivity of nanostructures can be obtained using atomistic classical Molecular Dynamics (MD) simulations, particularly for semiconductors where there is no significant contribution from electrons to thermal conduction. In this work, we obtain and analyze the thermal conductivity of amorphous carbon (aC) nanowires (NW) with a 2 nm radius and aC nanotubes (NT) with 0.5, 1 and 1.3 nm internal radii and a 2 nm external radius. The behavior of thermal conductivity with internal radii, temperature and density (related to different levels of sp3 hybridization), is compared with experimental results from the literature. Reasonable agreement is found between our modeling results and the experiments for aC films. In addition, in our simulations, the bulk conductivity is lower than the NW conductivity, which in turn is lower than the NT conductivity. NTs thermal conductivity can be tailored as a function of the wall thickness, which surprisingly increases when the wall thickness decreases. While the vibrational density of states (VDOS) is similar for bulk, NW and NT, the elastic modulus is sensitive to the geometrical parameters, which can explain the enhanced thermal conductivity observed for the simulated nanostructures. Thermal conductivity ; Materials science ; Nanowire ; Thermal conduction ; Carbon nanotube ; Conductivity ; Amorphous solid ; RADIUS ; Molecular dynamics ; Nanostructure ; Condensed matter physics ; Composite material ; Nanotechnology ; Crystallography ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites
Authors: Xiaohong Wu | Tingfei Zhang | Bin Wu | Haoxiang Zhou
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy Excessive pesticide residues on Chinese cabbage will be harmful to people’s health. Therefore, an identification system was designed for qualitative analysis of lambda-cyhalothrin residues on Chinese cabbage leaves. In order to extract discriminant information from mid-infrared (MIR) spectra of Chinese cabbage effectively, fuzzy uncorrelated discriminant vector (FUDV) analysis was proposed by introducing the fuzzy set theory into uncorrelated discriminant vector (UDV) analysis. In this system, the Cary 630 FTIR spectrometer was used to scan four samples of Chinese cabbage with different concentrations of lambda-cyhalothrin. The MIR spectra were preprocessed by standard normal variable (SNV) and Savitzky-Golay smoothing (SG). Next, the high-dimensional MIR spectra were processed for dimension reduction by principal component analysis (PCA). Furthermore, UDV, FUDV, and some other discriminant analysis algorithms were used for feature extraction, respectively. Finally, the K-nearest neighbor (KNN) classifier was employed to classify the data. The experimental results showed that when FUDV was used as the feature extraction algorithm, the identification system reached the maximum classification accuracy of 100%. The results indicated that FUDV combined with MIR spectroscopy was an effective method to identify lambda-cyhalothrin residues on Chinese cabbage. Keywords: Chinese cabbage, mid-infrared spectroscopy, fuzzy uncorrelated discriminant vector, uncorrelated discriminant vector, lambda-cyhalothrin residues DOI: 10.25165/j.ijabe.20221503.6486 Citation: Wu X H, Zhang T F, Wu B, Zhou H X. Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy. Int J Agric & Biol Eng, 2022; 15(3): 217–224. Linear discriminant analysis ; Mathematics ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Discriminant ; Uncorrelated ; Statistics ; Computer science Spectroscopy and Chemometric Analyses
Authors: Yi He | Kaifeng Liu | Lu Han | Weiwei Han
Source: International Journal of Molecular Sciences (article, 2022-09-05)
Field: Chemistry — Organic Chemistry

Clustering Analysis, Structure Fingerprint Analysis, and Quantum Chemical Calculations of Compounds from Essential Oils of Sunflower (Helianthus annuus L.) Receptacles Sunflower (<i>Helianthus annuus</i> L.) is an appropriate crop for current new patterns of green agriculture, so it is important to change sunflower receptacles from waste to useful resource. However, there is limited knowledge on the functions of compounds from the essential oils of sunflower receptacles. In this study, a new method was created for chemical space network analysis and classification of small samples, and applied to 104 compounds. Here, t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensions were used to reduce coordinates as node locations and edge connections of chemical space networks, respectively, and molecules were grouped according to whether the edges were connected and the proximity of the node coordinates. Through detailed analysis of the structural characteristics and fingerprints of each classified group, our classification method attained good accuracy. Targets were then identified using reverse docking methods, and the active centers of the same types of compounds were determined by quantum chemical calculation. The results indicated that these compounds can be divided into nine groups, according to their mean within-group similarity (MWGS) values. The three families with the most members, i.e., the d-limonene group (18), α-pinene group (10), and γ-maaliene group (nine members) determined the protein targets, using PharmMapper. Structure fingerprint analysis was employed to predict the binding mode of the ligands of four families of the protein targets. Thence, quantum chemical calculations were applied to the active group of the representative compounds of the four families. This study provides further scientific information to support the use of sunflower receptacles. Sunflower ; Helianthus annuus ; Hierarchical clustering ; Biological system ; Fingerprint (computing) ; Quantum chemical ; Cluster analysis ; Similarity (geometry) ; Chemistry ; Mathematics ; Principal component analysis ; Pattern recognition (psychology) ; Molecule ; Computational chemistry ; Biology ; Computer science ; Artificial intelligence ; Statistics ; Combinatorics ; Organic chemistry Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Computational Drug Discovery Methods
Authors: Main Astronomical Observatory of the NAS of Ukraine | I. B. Vavilova | Vladislav Khramtsov | D. V. Dobrycheva | M. Yu. Vasylenko | A. Elyiv | О. V. Мelnyk
Source: Kosmìčna nauka ì tehnologìâ (article, 2022-02-28)
Field: Chemistry — Analytical Chemistry

Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02&lt;z&lt;0.1 We applied the image-based approach with a convolutional neural network (CNN) model to the sample of low-redshift galaxies with –24m&lt;Mr&lt;–19.4m from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. To determine the principal parameters of galaxy morphology defined within the GZ2 project, we classified the galaxies into five visual types and 34 morphological features of galaxies from the inference dataset, which do not match with GZ2 training dataset. As a result, we created the morphological catalog of 315782 galaxies at 0.02&lt;z&lt;0.1, where these classes and features were defined for the first time for 216148 galaxies by image-based CNN classifier. For the rest of galaxies the initial morphological classification was re-assigned as in the GZ2 project. Main results are presented in the catalog of 19468 completely rounded, 27321 rounded in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general low-redshift galaxies of the studied SDSS sample. Our method shows the promising performance of morphological classification attaining &gt;93 % of accuracy for five classes morphology prediction except the cigar-shaped (~75 %) and completely rounded (~83 %) galaxies. As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 92–99 % depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. As a result, for the first time we assigned 34 morphological detailed features (bar, rings, number of spiral arms, mergers, etc.) for more than 160000 low-redshift galaxies from the SDSS DR9. We demonstrate that implication of the CNN model with adversarial validation and adversarial image data augmentation improves classification of smaller and fainter SDSS galaxies with mr &lt;17.7. Astrophysics ; Galaxy ; Physics ; Convolutional neural network ; Redshift ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Gamma-ray bursts and supernovae