jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. Pontikis | Gianguido Baldinozzi
Source: Preprints.org (preprint, 2022-04-28)
Field: Materials Science — Materials Chemistry

Phenomenological Potentials for the Refractory Metals Cr, Mo and W Cohesion in the refractory metals Cr, Mo, and W is phenomenologically described in this work via a n-body energy functional with a set of physically motivated parameters that were optimized to reproduce selected experimental properties characteristic of perfect and defective crystals. The functional contains four terms accounting for the hard-core repulsion, the Thomas-Fermi kinetic energy repulsion and for contributions to the binding energy of s and d valence electrons. Lattice dynamics, molecular statics, and molecular dynamics calculations show that this model describes satisfactorily thermodynamic properties of the studied metals whereas, unlike other empirical approaches from the literature, predictions of phonon dispersion relations and of surface and point defect energetics reveal in fair good agreement with experiments. These results suggest that the present model is well adapted to large-scale simulations and whenever total energy calculations of thermodynamic properties are unfeasible Phonon ; Molecular dynamics ; Valence electron ; Work (physics) ; Refractory metals ; Statistical physics ; Thermodynamics ; Electron ; Kinetic energy ; Physics ; Condensed matter physics ; Chemistry ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Chemical Physics Studies
Authors: Faisal Almisned
Source: Research Square (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

Computer Simulation for Shielding Properties of SiC and TiC Reinforced Composites Electromagnetic shielding ; Materials science ; Composite material ; Composite number ; Attenuation ; Radiation shielding ; Attenuation coefficient ; Radiation ; Nuclear physics ; Optics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: María Verónica Gerbaldo | Mónica E. Crivello | Silvia N. Mendieta | Verónica R. Elías
Source: AJEA (article, 2022-10-03)
Field: Chemistry — Analytical Chemistry

Degradación de diclofenac sódico mediante oxidación húmeda catalítica empleando CoFe2O4 En este trabajo se presenta el estudio de la degradación de diclofenac sódico en solución acuosa empleando oxidación húmeda catalítica. Se investigaron los parámetros de operación tales como la alimentación del reactor, flujo de oxidante y temperatura de reacción. Las reacciones experimentales se desarrollaron en un reactor de lecho fijo utilizando oxígeno puro y ferrita de cobalto como catalizador. Las condiciones óptimas para el proceso resultaron: concentración de contaminante= 40 mg/L, flujo de oxígeno = 150 mL/L, alimentación del reactor = 0,25 mL/L y temperatura de reacción = 200°C. En estas condiciones se degradó el 97,59% del fármaco en un proceso continuo. También se estudió la cinética de reacción. El proceso pudo describirse mediante una cinética de pseudo-primer orden. La energía de activación aparente se calculó en 20,75 kJ/mol. Los resultados indican que este proceso podría utilizarse como método de tratamiento para aguas residuales que contengan compuestos farmacéuticos. Chemistry ; Humanities ; Diclofenac ; Nuclear chemistry ; Medicinal chemistry ; Philosophy ; Biochemistry Analytical Methods in Pharmaceuticals ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Alexander S. Davis | Vinamra Agrawal
Source: arXiv (Cornell University) (preprint, 2022-09-22)
Field: Materials Science — Ceramics and Composites

Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic-continuum framework Despite their success in microscale modeling of materials, atomistic methods are still limited by short time scales, small domain sizes, and high strain rates. Multiscale formulations can capture the continuum-level response of solids over longer runtimes, but using such schemes to model highly dynamic, nonlinear phenomena is very challenging and an active area of research. In this work, we develop novel techniques within the concurrent atomistic-continuum multiscale framework to simulate shock wave propagation through a two-dimensional, single-crystal lattice. The technique is described in detail, and two moving window methods are incorporated to track the shock front through the domain and thus prevent spurious wave reflections at the atomistic-continuum interfaces. We compare our simulation results to analytical models as well as previous atomistic and CAC data and discuss the apparent effects of lattice orientation on the shock response of FCC crystals. We then use the moving window techniques to perform parametric studies which analyze the shock front's structure and planarity. Finally we compare the efficiency of our model to molecular dynamics simulations. This work showcases the power of using a moving window concurrent multiscale framework to simulate dynamic shock evolution over long runtimes and opens the door to more complex studies involving shock propagation through composites and high-entropy alloys. Microscale chemistry ; Shock wave ; Spurious relationship ; Nonlinear system ; Statistical physics ; Molecular dynamics ; Multiscale modeling ; Parametric statistics ; Planarity testing ; Lattice (music) ; Shock (circulatory) ; Mechanics ; Computer science ; Physics ; Mathematics Advanced ceramic materials synthesis ; High-pressure geophysics and materials ; Diamond and Carbon-based Materials Research
Authors: Pukar Khanal | Hullatti Kiran Kumar
Source: Journal of Applied Pharmaceutical Science (article, 2022-03-05)
Field: Chemistry — Analytical Chemistry

Application of neural networks in the identification of pharmaceutical lead hits Artificial neural networks (ANNs) are also known as a digitalized model of the mammalian brain and are used in pattern reorganization, including machine translation and speech reorganization. In addition, some attempts have been made to implement it in new drug design or discovery. Presently, the drug development process utilizes the complex approach in identifying a single lead hit which often fails multiple times due to its poor pharmacokinetic properties and severe side effects. This could be the outcome of poor screening approaches for hit candidates and neglecting the probable bias. However, ANNs can be helpful in decision-making for many researchers, as well as in clinical application. Also, for medicinal chemists, it may act as an effective tool to pick lead hit and predict the 3D protein confirmation to evaluate the effectiveness of the selected lead hit. The present study briefs on ANNs that can be used as a predictive tool to classify diseases, in vitro and in vivo data correlation, target identification, absorption, distribution, metabolism, excretion, and toxicity profiling, and its application in modern drug discovery. Artificial neural network ; Identification (biology) ; Artificial intelligence ; Mathematics ; Computer science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals
Authors: Benjamin T. Boyle | Jeffrey N. Levy | Louis de Lescure | Robert S. Paton | Andrew McNally
Source: ChemRxiv (preprint, 2022-07-06)
Field: Chemistry — Organic Chemistry

3-Selective Halogenation of Pyridines via Zincke Imine Intermediates Pyridine halogenation reactions are crucial for obtaining the vast array of derivatives required for drug and agrochemical development. Yet, despite more than a century of synthetic endeavors, there are no broadly applicable 3-selective halogenation processes that function directly from pyridine C–H precursors. We have developed a ring-opening, halogenation, ring-closing sequence that temporarily transforms pyridines into a reactive series of alkenes. These Zincke imine intermediates undergo highly regioselective halogenation reactions under mild conditions. Experimental and computational mechanistic studies indicate that the selectivity-determining step changes based on the halogen electrophile. Using this method, we formed a diverse set of 3-halopyridines and demonstrated late-stage halogenation of complex pharmaceuticals and agrochemicals. Halogenation ; Regioselectivity ; Chemistry ; Imine ; Electrophile ; Pyridine ; Ring (chemistry) ; Combinatorial chemistry ; Selectivity ; Halogen ; Organic chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Advanced Synthetic Organic Chemistry ; Synthesis and Catalytic Reactions
Authors: Likai Du
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-04-06)
Field: Chemistry — Inorganic Chemistry

Understanding the Spin Crossover Dynamical Effects of the Dioxygen Binding and Activation on HOD enzyme Abstract For the cofactor-free 1-H-3-hydroxy-4-oxoquinaldine-2,4-dioxygenase (HOD), the dioxygen (O 2 ) dependent steps are rate-limiting along with a spin state crossover to the singlet spin state. Here, the primary triplet O 2 molecule activation on the 2-methyl-3-hydroxy-4(1H)-quinolone (MHQ) is investigated, and the catalytic role of the intersystem crossing effects is highlighted by directly comparing results from the Born-Oppenheimer dynamics and non-adiabatic surface hopping dynamics. This work confirms non-adiabatic dynamical effects are essential to modulate the O 2 activation on the substrate MHQ. The time scale of the equilibration and conversion from triplet to singlet state should be in the range of a few hundreds of femtoseconds. We hope this work provides us a fresh look at the underlying physics of dioxygen activation reactions involving more than one spin state. Intersystem crossing ; Singlet state ; Chemistry ; Adiabatic process ; Spin states ; Spin (aerodynamics) ; Triplet state ; Spin crossover ; Physics ; Chemical physics ; Photochemistry ; Molecule ; Atomic physics ; Quantum mechanics ; Excited state ; Thermodynamics ; Crystallography Metal-Catalyzed Oxygenation Mechanisms ; Photosynthetic Processes and Mechanisms ; Photochemistry and Electron Transfer Studies
Authors: Alexander Kovacs | Lukas Exl | Alexander Kornell | Johann Fischbacher | Markus Hovorka | Markus Gusenbauer | Leoni Breth | Harald Oezelt | Masao Yano | Noritsugu Sakuma | Akihito Kinoshita | Tetsuya Shoji | Akira Kato | T. Schrefl
Source: arXiv (Cornell University) (preprint, 2022-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets created by grain growth and micromagnetic simulations. We show that the granular structure of a magnet can be encoded within a low-dimensional latent space. Latent codes are constructed using a variational autoencoder. The mapping of structure code to hysteresis properties is a multi-target regression problem. We apply deep neural network and use parameter sharing, in order to predict anchor points along the demagnetization curves from the magnet's structure code. The method is applied to study the magnetic properties of nanocrystalline permanent magnets. We show how new grain structures can be generated by interpolation between two points in the latent space and how the magnetic properties of the resulting magnets can be predicted. Magnet ; Nanocrystalline material ; Hysteresis ; Materials science ; Demagnetizing field ; Interpolation (computer graphics) ; Artificial neural network ; Condensed matter physics ; Autoencoder ; Magnetic hysteresis ; Algorithm ; Statistical physics ; Computer science ; Artificial intelligence ; Physics ; Mechanical engineering ; Nanotechnology ; Magnetization ; Magnetic field ; Engineering Magnetic Properties and Applications ; Magnetic properties of thin films ; Machine Learning in Materials Science
Authors: Melika Masoudi | Davood Mansury | Amirhossein Tashakor
Source: Journal of Current Biomedical Reports (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

An overview of nanoparticle production from plant gums and their action as antimicrobial agents There have been various chemicals and routines for the treatment of infections. The extensive use of antibiotics has led to serious issues including antibiotic resistance and serious side effects. The use of plant gum nanoparticles (NPs) is one of the several ways that can be employed greatly for the treatment of infections and have gained so much popularity by the scientists recently due to their several advantages over chemicals including being nontoxic and providing better tolerance to the patient. Several studies have been performed recently, stressing the undeniable advantages of these substances in the treatment of illnesses compared with their chemical counterparts. There are studies suggesting that these NPs have great potential in the treatment of multi-drug-resistant bacteria and that these substances have great anti-cancer effects due to their anti-inflammatory roles. Among various plant gums, Gum Arabia, gum Karaya, Kondagogu gum, and gum Tragacanth, Guar gum, and gum Ghatti have gathered more interest as anti-inflammatory subjects for studies because of their several pros including having more tissue bio-availability, being easy to use, etc. The use of plant gums can be limited due to a series of disadvantages but this can be untangled by using natural nanoparticles which can be synthesized via several ways including ultrasonic irradiation, etc. Among various metallic NPs, the most frequent of them in these studies are Silver nanoparticles (AgNPs) and Gold nanoparticles (AuNPs). According to these studies, AgNPs have a more bactericidal effect than AuNPs which is due to them being more of an antioxidant. Antimicrobial ; Production (economics) ; Action (physics) ; Anti-Infective Agents ; Nanotechnology ; Biochemical engineering ; Biotechnology ; Business ; Chemistry ; Materials science ; Biology ; Engineering ; Economics ; Physics ; Organic chemistry Nanoparticles: synthesis and applications ; Polysaccharides Composition and Applications ; Therapeutic Uses of Natural Elements
Authors: Jonathan A. Semelak | Nicolás O. Foglia | Darío A. Estrı́n
Source: ChemRxiv (preprint, 2022-06-20)
Field: Materials Science — Materials Chemistry

Minimum free energy pathways with the nudgedelastic band method in combination with aQM-MM Hamiltonian The optimization of minimum free energy pathways (MFEP) is one of the most widely used strategies to study activated processes. For chemical reactions, this requires the use of quantum mechanics. Using quantum mechanics molecular mechanics (QM- MM) Hamiltionians allows the simulation of reactive processes in complex environments by treating with quantum mechanics only the chemically relevant part of the system. However, even within this approximation, the affordable simulation lenghts of QM-MM simulations is in general, quite limited. Free energy methods based on the sampling of the potential energy surface require long simulations times to provide converged and accurate results. As consequence, the combination of QM-MM methods and free energy calculations is computationally expensive. Moreover, the user usually needs to perform an a priori selection of the reaction coordinate. This may be not trivial for the general case. One of the most established methods for finding potential energy profiles without selecting a reaction coordinate is the nudged elastic band method (NEB). In this work, we used the extension of this method to the exploration of the free energy surface for finding MFEP (FENEB). We present and apply to reactive systems an improved version of the basic optimization scheme of FENEB that increases its robustness, and is based on decoupling the optimization of the band in the perpendicular direction to the band, from the optimization of the tangential direction. In each optimization step, a full optimization with the spring force is performed, in order to keep the images evenly distributed. Additionally, we evaluate the influence of sampling in the quality of the optimized MFEP and free energy barrier computed from it. We show and discuss that the FENEB method provides a good estimation of the reaction barrier even with relatively short simulations lenghts and that it scales better than umbrella sampling both with simulation lenght and with dimensionality. Overall, our results support that the combination of QM-MM methods and the FENEB provides an adequate tool study chemical processes in complex environments. Reaction coordinate ; Potential energy surface ; Hamiltonian (control theory) ; Robustness (evolution) ; Molecular dynamics ; A priori and a posteriori ; Quantum ; QM/MM ; Computer science ; Statistical physics ; Decoupling (probability) ; Potential energy ; Physics ; Classical mechanics ; Quantum mechanics ; Mathematical optimization ; Chemistry ; Computational chemistry ; Mathematics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mohammad Kianezhad | Mehrdad Youzi | Mehran Vaezi | Hossein Nejat Pishkenari
Source: Research Square (preprint, 2022-09-09)
Field: Materials Science — Materials Chemistry

Unidirectional Motion of C60-based Nanovehicles Using Hybrid Substrates with Temperature Gradient Abstract The synthesis of nanocar structures proposes the idea of transporting payloads on the surface. To eliminate the concern of diffusive surface motion of nanocars, in this study, we evaluate the motion of C 60 and C 60 -based nanovehicles on graphene, and hexagonal boron-nitride (BN) surfaces using molecular dynamics simulations and potential energy analysis. Utilizing the graphene-hBN hybrid substrate, it has been indicated that C 60 is more stable on boron-nitride impurity regions in the graphene substrate and an energy barrier restricts the motion to the boron-nitride impurity. Increasing the temperature causes the molecule to overcome the energy barrier frequently. A nanoroad of boron-nitride with graphene sideways is designed to confine the surface motion of C 60 and nanovehicles at 300 K. As expected, the motion of all surface molecules is limited to the boron-nitride nanoroads. Although the motion is restricted to the boron-nitride nanoroad, the diffusive motion is still noticeable in lateral directions. To obtain the unidirectional motion for C 60 and nanocars on the surface, a temperature gradient is applied to the surface. The unidirectional transport to the regions with a lower temperature of the nanoroad occurs in a short period of time, due to the lower energies of molecules on the colder parts. Graphene ; Boron nitride ; Materials science ; Chemical physics ; Substrate (aquarium) ; Hexagonal boron nitride ; Boron ; Nanotechnology ; Temperature gradient ; Molecule ; Condensed matter physics ; Chemistry ; Physics ; Organic chemistry Graphene research and applications ; Molecular Junctions and Nanostructures ; Fullerene Chemistry and Applications
Authors: Jungmin Lee | Sang Hyun Lee | Jinyoung Hwang
Source: Research Square (preprint, 2022-04-11)
Field: Chemistry — Organic Chemistry

Topological characterization for the geometric origin of transition in electrical conductivity for hybrid nanocomposites Abstract In this study, the analytical relationship between the electrical conductivity of hybrid nanocomposites consisting of conducting nanowires and insulating particulate fillers was investigated. We measured the performance as a function of the major factors defined by the combination of physical parameters of the constituent fillers. The major factors were identified using data mining techniques. The topological structure of the nanocomposites was described using a combination of Voronoi tessellation and the Swiss cheese model. Based on the geometrical framework, an analytical model of the electrical conductivity of the hybrid nanocomposites was built based on the Kozeny–Carman approach. The model delineates a one-dimensional characterization of the transition in electrical conductivity associated with the size ratio of the fillers and enables to visualize the quantitative relationship between the topological structure and electrical conductivity of the hybrid nanocomposites. Formalism facilitates the design of the physical properties of hybrid nanocomposites before their synthesis. Nanocomposite ; Materials science ; Voronoi diagram ; Electrical resistivity and conductivity ; Conductivity ; Characterization (materials science) ; Formalism (music) ; Nanowire ; Topology (electrical circuits) ; Nanotechnology ; Composite material ; Physics ; Mathematics ; Geometry Polydiacetylene-based materials and applications ; Topological and Geometric Data Analysis
Authors: Rinske M. Alkemade | Emanuele Boattini | Laura Filion | Frank Smallenburg
Source: The Journal of Chemical Physics (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Comparing machine learning techniques for predicting glassy dynamics In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms-linear regression, neural networks, and graph neural networks-to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train, making it by far the method of choice. Machine learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Set (abstract data type) ; Regression ; Dynamics (music) ; Binary number ; Linear regression ; Mathematics ; Statistics Material Dynamics and Properties ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Saad Aldin Mohamed | Yeong-Jin Kim | Junkee Lee | Wonyoung Choe | Jihan Kim
Source: ChemRxiv (preprint, 2022-03-17)
Field: Chemistry — Inorganic Chemistry

Understanding the Structural Collapse During Activation of Metal–Organic Frameworks with Copper Paddlewheels Many metal-organic frameworks (MOFs) suffer from stability issues as they can be easily amorphized from various external stimuli. In particular, it is common to observe structural collapse during the activation process of removing the synthesis solvent. In this study, we conduct high-throughput computational analysis that focuses on activation status of MOFs that possess copper paddlewheel metal nodes. From the analysis, various mechanical properties (e.g. bulk, young, and shear moduli) were found to be good predictors for collapse. Furthermore, we have identified anomalies MOFs with good mechanical stability that were previously reported to collapse. Accordingly, the activation process was re-attempted with improved techniques, and these MOFs were successfully activated. Copper ; Materials science ; Metal ; Shear (geology) ; Moduli ; Solvent ; Nanotechnology ; Chemistry ; Composite material ; Metallurgy ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques