jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael A. Skinnider | R. Greg Stacey | David S. Wishart | Leonard J. Foster
Source: ChemRxiv (preprint, 2021-01-27)
Field: Materials Science — Materials Chemistry

Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space Deep generative models are powerful tools for the exploration of chemical space, enabling the on-demand gener- ation of molecules with desired physical, chemical, or biological properties. However, these models are typically thought to require training datasets comprising hundreds of thousands, or even millions, of molecules. This per- ception limits the application of deep generative models in regions of chemical space populated by only a small number of examples. Here, we systematically evaluate and optimize generative models of molecules for low-data settings. We carry out a series of systematic benchmarks, training more than 5,000 deep generative models and evaluating over 2.6 billion generated molecules. We find that robust models can be learned from far fewer examples than has been widely assumed. We further identify strategies that dramatically reduce the number of molecules required to learn a model of equivalent quality, and demonstrate the application of these principles by learning models of chemical structures found in bacterial, plant, and fungal metabolomes. The structure of our experiments also allows us to benchmark the metrics used to evaluate generative models themselves. We find that many of the most widely used metrics in the field fail to capture model quality, but identify a subset of well-behaved metrics that provide a sound basis for model development. Collectively, our work provides a foundation for directly learning generative models in sparsely populated regions of chemical space. Chemical space ; Computer science ; Generative model ; Generative grammar ; Benchmark (surveying) ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Space (punctuation) ; Quality (philosophy) ; Deep learning ; Drug discovery ; Mathematics ; Bioinformatics ; Biology ; Geography Machine Learning in Materials Science ; Genomics and Phylogenetic Studies ; Microbial Natural Products and Biosynthesis
Authors: Zijiang Yang | Dipendra Jha | Arindam Paul | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: arXiv (Cornell University) (preprint, 2021-01-26)
Field: Materials Science — Materials Chemistry

A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in scientific applications: forward and inverse. While the forward modeling estimates the observations based on known parameters, the inverse modeling attempts to infer the parameters given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the parameters that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, healthcare and materials science. However, it is challenging to solve inverse problems, because they usually need to learn a one-to-many non-linear mapping, and also require significant computing time, especially for high-dimensional parameter space. Further, inverse problems become even more difficult to solve when the dimension of input (i.e. observation) is much lower than that of output (i.e. parameters). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling, and it is evaluated on a materials science dataset for microstructural materials design. Compared with baseline methods, the results demonstrate that the proposed framework can overcome the above-mentioned challenges and produce multiple promising solutions in an efficient manner. Inverse ; Generative grammar ; Inverse problem ; Computer science ; Dimension (graph theory) ; Adversarial system ; Mathematical optimization ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Composite Material Mechanics
Authors: Xiangzhe Kong | Zhixing Tan | Yang Liu
Source: arXiv (Cornell University) (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures. Molecular graph generation is a fundamental but challenging task in various applications such as drug discovery and material science, which requires generating valid molecules with desired properties. Auto-regressive models, which usually construct graphs following sequential actions of adding nodes and edges at the atom-level, have made rapid progress in recent years. However, these atom-level models ignore high-frequency subgraphs that not only capture the regularities of atomic combination in molecules but also are often related to desired chemical properties. In this paper, we propose a method to automatically discover such common substructures, which we call {\em graph pieces}, from given molecular graphs. Based on graph pieces, we leverage a variational autoencoder to generate molecules in two phases: piece-level graph generation followed by bond completion. Experiments show that our graph piece variational autoencoder achieves better performance over state-of-the-art baselines on property optimization and constrained property optimization tasks with higher computational efficiency. Leverage (statistics) ; Molecular graph ; Autoencoder ; Computer science ; Graph ; Theoretical computer science ; Property (philosophy) ; Algorithm ; Artificial intelligence ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Prasad C. Walimbe | Preeti S. Kulkarni | Sunil D. Kulkarni
Source: Chemical Papers (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Obstruction scaling model for the diffusion of the outer electrolyte leading to Liesegang patterns of (AgNO3 + KCl) system in agarose hydrogel Agarose ; Electrolyte ; Diffusion ; Self-healing hydrogels ; Scaling ; Volume (thermodynamics) ; Chemistry ; Chemical physics ; Materials science ; Thermodynamics ; Polymer chemistry ; Chromatography ; Physical chemistry ; Physics ; Electrode Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Electrostatics and Colloid Interactions
Authors: Yudie Zhang | Long Li | Jizeng Wang
Source: Membranes (article, 2021-12-19)
Field: Materials Science — Biomaterials

Role of Ligand Distribution in the Cytoskeleton-Associated Endocytosis of Ellipsoidal Nanoparticles Nanoparticle (NP)-cell interaction mediated by receptor-ligand bonds is a crucial phenomenon in pathology, cellular immunity, and drug delivery systems, and relies strongly on the shape of NPs and the stiffness of the cell. Given this significance, a fundamental question is raised on how the ligand distribution may affect the membrane wrapping of non-spherical NPs under the influence of cytoskeleton deformation. To address this issue, in this work we use a coupled elasticity-diffusion model to systematically investigate the role of ligand distribution in the cytoskeleton-associated endocytosis of ellipsoidal NPs for different NP shapes, sizes, cytoskeleton stiffness, and the initial receptor densities. In this model, we have taken into account the effects of receptor diffusion, receptor-ligand binding, cytoskeleton and membrane deformations, and changes in the configuration entropy of receptors. By solving this model, we find that the uptake process can be significantly influenced by the ligand distribution. Additionally, there exists an optimal state of such a distribution, which corresponds to the fastest uptake efficiency and depends on the NP aspect ratio and cytoskeleton stiffness. We also find that the optimal distribution usually needs local ligand density to be sufficiently high at the large curvature region. Furthermore, the optimal state of NP entry into cells can tolerate slight changes to the corresponding optimal distribution of the ligands. The tolerance to such a change is enhanced as the average receptor density and NP size increase. These results may provide guidelines to control NP-cell interactions and improve the efficiency of target drug delivery systems. Endocytosis ; Cytoskeleton ; Biophysics ; Ligand (biochemistry) ; Receptor ; Stiffness ; Cell membrane ; Chemistry ; Cell ; Nanotechnology ; Cell biology ; Materials science ; Physics ; Biology ; Biochemistry ; Thermodynamics Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Lipid Membrane Structure and Behavior
Authors: F. M. Rodríguez-Zamarripa | I. Trejo-Carrizalez | Elsa Cervantes‐González
Source: International Journal of Environmental Science and Technology (article, 2021-03-01)
Field: Chemistry — Analytical Chemistry

Diclofenac sodium removal from aqueous phase using a keratinolytic waste and biodegradation of the adsorbate/adsorbent complex Biodegradation ; Chemistry ; Adsorption ; Aqueous solution ; Chromatography ; Langmuir adsorption model ; Nuclear chemistry ; Organic chemistry Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jing Wang | Keenan Zhuo | Jianping Gao | Uzi Landman | M. Y. Chou
Source: Physical Review Materials (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Mechanism for anisotropic diffusion of liquid-like Cu atoms in hexagonal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>β</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mi mathvariant="normal">Cu</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">S</mml:mi></mml:math> ${\mathrm{Cu}}_{2}\mathrm{S}$ in its high-temperature phases is a promising candidate for thermoelectric materials with the combination of a solid S lattice and liquidlike Cu atoms in the context of the so-called phonon-liquid electron-crystal (PLEC) mechanism. The atomic dynamics associated with the high Cu mobility is a critical component in our understanding of the low thermal conductivity in these materials. Among various possible PLEC compounds that have been studied experimentally, hexagonal $\ensuremath{\beta}\ensuremath{-}{\mathrm{Cu}}_{2}\mathrm{S}$ is a unique case that exhibits anisotropic diffusion channels for Cu atoms in the presence of crystalline S layers. To unveil the diffusion mechanism for such liquidlike Cu atoms, we present first-principles molecular dynamics simulations over 50 ps for hexagonal ${\mathrm{Cu}}_{2}\mathrm{S}$ at 450 K. Quantitative analysis of the atomic radial distributions, mean-square displacements, velocity autocorrelations, and atomic trajectories is reported and confirms the liquid-solid hybrid features. Our simulations reveal the preference of threefold triangular sites by Cu atoms in the S lattice. The triangular sites in the interlayer region do not bind Cu atoms strongly, allowing them to diffuse in the horizontal direction between S layers like a mobile liquid with a calculated diffusion coefficient $\ensuremath{\sim}5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}\phantom{\rule{4pt}{0ex}}{\mathrm{cm}}^{2}$/s. The corresponding atomic trajectories have a wide spread and cannot be described by the Chudley-Elliott jump diffusion model. In contrast, Cu atoms are more strongly confined at the triangular sites within the S planes, and Cu diffusion takes place only when the atom hops out of the S layer and enters the interlayer region. This yields a 50% smaller diffusion coefficient in the vertical direction. The anisotropic diffusion channels for liquidlike Cu atoms in hexagonal $\ensuremath{\beta}\ensuremath{-}{\mathrm{Cu}}_{2}\mathrm{S}$ may provide an additional degree of freedom in designing promising systems for future energy applications. Materials science ; Crystallography ; Lattice diffusion coefficient ; Anisotropy ; Crystal structure ; Context (archaeology) ; Hexagonal lattice ; Condensed matter physics ; Physics ; Effective diffusion coefficient ; Chemistry Advanced Thermoelectric Materials and Devices ; Physics of Superconductivity and Magnetism ; Quantum Dots Synthesis And Properties
Authors: Panagiotis Stamatopoulos | Myrto Zeneli | A. Nikolopoulos | A. Bellucci | D.M. Trucchi | Nikolaos Nikolopoulos
Source: Energy Conversion and Management (article, 2021-01-13)
Field: Materials Science — Materials Chemistry

Introducing a 1D numerical model for the simulation of PN junctions of varying spectral material properties and operating conditions Spectral properties ; Materials science ; Computer simulation ; Computer science ; Mechanics ; Physics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Thermal Radiation and Cooling Technologies
Authors: alshimaa gomaa | rania saleh | Asrar Elsayed | Ahmed M. Daher
Source: Egyptian Journal of Chemistry (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Green Synthesis of copper Nanoparticles: Synthesis, Characterization and their application: About Future In the present study copper oxide nanoparticles (CuNPs) were synthesized via simple and eco-friendly green route using black tea extract . Characterization of synthesized nanoparticles (NPs) was undertaken. The characteristic absorption peak of CuNPs was in range (352-355) nm in UV–Vis spectrum. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) studies revealed the morphological and structural character of green NPs. The particle size was 25-50 nm. Energy dispersive spectroscopy (EDX) showed high intense metallic peak of copper (Cu), oxygen (O), carbon (C) and low intense peaks of phosphorus (P) , calcium (Ca) , sulfur (S), potassium (K) elements due to the capping action of biomolecules of plant extract in CuNPs formation. The X-ray diffraction (XRD) pattern showed distinctive peaks corresponding to (200), (220) and (311) planes revealing the high crystalline nature of synthesized CuNPs . The dyeing behavior of green CuNPs treated fabric with Acid Black 172 (AB 172) has been studied and the build up of dye, measured as exhaustion percentage (E%). Dye adsorption rate constants according to pseudo-first order, pseudo-second order, and intra-particle diffusion kinetic models were calculated. Moreover, the dye adsorption equilibrium data were fitted well to the Freundlich isotherm rather than Langmuir isotherm. The overall adsorption process follow pseudo-second order kinetics, Intraparticle diffusion and Elovich model. The CuNPs treatment produces a nylon fabric with advanced color fastness and antibacterial properties enabling them to improve human health care and reducing temperature, the environmental impacts, fabric damage, amount of dyestuff used and saving energy of conventional dyeing of nylon fabrics. This review focuses on green synthesis of CuNPs using environmentally benign reagents in minimal time paves the way for future studies on CuNPs toxicity without risking interference from potentially toxic reagents and capping agents. The use of this technique to treat nylon fabric may lead to new coloration technique and other functional improvement. Characterization (materials science) ; Copper ; Nanoparticle ; Nanotechnology ; Materials science ; Metallurgy Nanoparticles: synthesis and applications
Authors: Jehanzeb Ali Shah | Cyrus Raza Mirza | Tayyab Ashfaq Butt | W. M. A. Khalifa | Hatem Gasmi | Hajira Haroon | Muhammad Saqib Khan | Muhammad Arif Ali | Iftikhar Zeb | Sajid Hussain Shah | Bilal Ahmad Zafar Amin | Muhammad Bilal
Source: Polish Journal of Environmental Studies (article, 2021-01-28)
Field: Chemistry — Analytical Chemistry

Tobacco Stalk Waste Biomass Holds Multilayerand Spontaneous Adsorption Capabilitiesfor Reactive Black 5 Dye: Equilibrium Modellingand Error Function Analysis Tobacco stalk biomass (TSB) is explored for reactive black 5 (RB5) dye treatment in batch reactor. TSB displayed RB5 adsorption efficiency of 93.5% at 120 min. The RB5 data were fitted to linear and non-linear isotherms and kinetic models. Error functions were determined to have a holistic view on optimum parameters and to validate prediction of models. Harkin-Jura exhibited better fit than that of Freundlich and Langmuir isotherms suggesting multilayer adsorption of RB5 onto TSB on the basis of R 2 and chi-square ( 2 ) error values. Adsorption kinetics was best explained by intraparticle diffusion model (IPD) at increasing initial concentration of RB5. Among the error functions evaluated, the 2 values for the IPD model were found to be smaller than those for PSO. This suggests that kinetics of RB5 dye is better controlled by IPD phenomenon. Fourier transform infrared spectroscopy confirms the physical adherence of dye to the adsorbent. Values of Gibbs free energy were found to be -19. 55 -21.41, -23.40 and -25.33 kJ/mol which shows spontaneous adsorption. Moreover, it is found that TSB adsorbent could be utilized as the effective and economically viable adsorbent for RB5 dye from aqueous solution. Stalk ; Adsorption ; Biomass (ecology) ; Function (biology) ; Biological system ; Chemistry ; Chemical engineering ; Pulp and paper industry ; Environmental science ; Waste management ; Materials science ; Chromatography ; Biology ; Engineering ; Organic chemistry ; Ecology ; Cell biology ; Horticulture Dye analysis and toxicity
Authors: Umme Ruman | Kalaivani Buskaran | Saifullah Bullo | Georgia Pastorin | Mas Jaffri Masarudin | Sharida Fakurazi | Mohd Zobir Hussein
Source: Research Square (preprint, 2021-02-04)
Field: Materials Science — Biomaterials

Sorafenib and 5-Fluorouracil Loaded Dual Drug Nanodelivery Systems for Hepatocellular Carcinoma and Colorectal Adenocarcinoma <title>Abstract</title> <bold>Purpose:</bold> Here, we reported the sysnthesis of two clinically used drugs, 5-fluorouracil (5FU) and Sorafenib (SF)-loaded in chitosan nanoparticles and their priliminary study of therapeutics effect on hepatocellular carcinoma and colorectal adenocarcinoma cell lines. We have formulated chitosan nanoparticles (CS NPs) loaded dual (SF and 5-FU) drugs nanodelivery system for SF/5FU-CS NPs and their coating version with folic acid (FA) for SF/5FU-CS-FA NPS. Human hepatocellular carcinoma (HepG2) and colorectal adenocarcinoma (HT29) cell lines were selected for in vitro cytotoxicity studies to evaluate the preliminary anticancer efficacy of both nanoparticles.<bold>Characterization</bold>: The physiochemical characterization of SF/5FU-CS NPs and SF/5FU-CS-FA NPs were investigated by DLS, FESEM, HRTEM, EDX, XRD, TGA, FTIR, and HPLC methods.<bold>Results:</bold> DLS study has shown the size of SF/5FU-CS and SF/5FU-CS-FA nanoparticles were about 78±14 nm and 142±25 nm, respectively. HRTEM and FESEM studies confirmed the spherical shape with size of 60-70nm for SF/5FU-CS and 90-150 nm for SF/5FU-CS-FA NPs. The XRD results indicated the drug loading and folate-coating comfirmation. FTIR peaks confirmed the presence of drugs in the nanoparticles, as well as folate-coating on the surface of the nanoparticles. TGA results demonstrated the thermostability of both nanoparticles. The release profiles of SF and 5FU from the two designed NPs were found to be in a sustained manner according to the pseudo-second-order kinetics model indicating a good delivery system for tumor cells. The cytotoxicity studies confirmed the better anti-cancer activity of the nanoparticles compared to the free 5-fluorouracil and sorafenib against liver cancer cells, HepG2 and colon cancer cells, HT29. Conversely, both NPs were found not toxic towards normal human dermal fibroblast cells (HDF) cells. Sorafenib ; Nanoparticle ; Nuclear chemistry ; Chemistry ; Hepatocellular carcinoma ; Drug delivery ; Fluorouracil ; High-resolution transmission electron microscopy ; Chitosan ; Materials science ; Nanotechnology ; Cancer research ; Medicine ; Cancer ; Internal medicine ; Biochemistry ; Organic chemistry ; Transmission electron microscopy Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; Cancer Research and Treatment
Authors: O. V. Perlova | И. С. Иванова | Yu. S. Dzyazko | М. О. Данилов | Igor A Rusetskii | G. Ya. Kolbasov
Source: Himia Fizika ta Tehnologia Poverhni (article, 2021-03-01)
Field: Chemistry — Inorganic Chemistry

Sorption of U(VI) compounds on inorganic composites containing partially unzipped multiwalled carbon nanotubes Unlike ion-exchange resins, inorganic sorbents possess high selectivity towards heavy metal ions and stability against ionizing radiation. However, sorption on these materials is rather slow. Moreover, sorption capacity strongly depends on the solution pH. In order to improve sorption properties of inorganic ion-exchangers, composites containing advanced carbon materials are obtained. Regularities of sorption of U(VI) compounds from low-concentrated aqueous solutions (up to 0.1 mmol dm–3 of uranium) on hydrated zirconium dioxide and zirconium hydrophosphate are considered. The sorbents were modified with partially unzipped multiwalled carbon nanotubes (PUMWCNTs). Sorption isotherms were obtained and analyzed. They obey Dubinin-Radushkevich model indicating sorption sites, a size of which is comparable with that of ions being sorbed. As found, the sorption mechanism is ion exchange. The effect of the solution pH on the sorption rate of U(VI) ions and capacity of inorganic ion-exchangers and their composites has been considered. Carbon additions increase sorption capacity of zirconium dioxide and zirconium hydrophosphate, when the initial pH of one-component solution is 3–4 and 5–7 respectively. Under these conditions, U(VI)-containing cations are removed from the solution practically completely. The rate of sorption obeys the model of chemical reaction of pseudo-second order, when uranium is removed from one-component solution. PUMWCNTs slow down sorption on zirconium dioxide and accelerate it on zirconium hydrophosphate. The dependence of the pseudo second order equation constants on the pH of U(VI) solutions was analyzed. The reaction of the first order occurs, when the solution contains also Ca2+ and Mg2+ ions. Regeneration was carried out using HNO3 and NaHCO3 solutions: the rate-determining stage of desorption is particle diffusion. It has been shown that one-component ZHP can be regenerated with a NaHCO3 solution practically completely. The most suitable solution for U(VI) desorption from ZHP-PUMWCNTs composite is a 1 M HNO3 solution. Sorption ; Zirconium ; Aqueous solution ; Ion exchange ; Inorganic chemistry ; Chemistry ; Ion ; Materials science ; Adsorption ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes