jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Zhou | Enrique R. Batista | Ping Yang | Danny Pérez
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman-Rubin Convergence Diagnostics The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are a powerful tool for computing this critical parameter. Nevertheless, the accurate and efficient calculation of solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. In the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman-Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare earth elements immersed in ionic liquids, a scenario where the aforementioned challenges render standard approaches ineffective. The proposed method proves effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short. Solvation ; Thermodynamic integration ; Statistical physics ; Convergence (economics) ; Molecular dynamics ; Gaussian process ; Computer science ; Energy (signal processing) ; Gaussian ; Chemistry ; Physics ; Ion ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Chongzhi jia | Hua Li | Zhongliang Yang | Rongchen Xu | Lijun Wang | Hongbo Li
Source: Research Square (preprint, 2023-03-13)
Field: Materials Science — Biomaterials

From medical strategy to food-borne prophylactic strategy: stabilizing dental collagen with aloin Abstract Objectives : We developed a food-borne prophylactic strategy with aloin to protect dental collagen through enhancing the mechanical strength, thermostability, and the resistance to enzymatic hydrolysis of dental collagen. The mechanism and effect of this food-borne prophylactic strategy were examined. Methods : Aloin solution (0.1 mg/mL) was prepared. The concentration is equivalent to the natural content of aloin in edible aloe. Attenuated total reflectance flourier transform infrared spectroscopy (ATR-FTIR), Gaussian peak fitting, and X-ray diffraction (XRD) were used to explore the mechanism of interaction between aloin and dental collagen in situ. Mechanical strength and thermostability were separately evaluated via Ultimate tensile strength test (UTS) and thermogravimetric analysis (TGA). Hydroxyproline (HYP), cross-linked carboxy-terminal telopeptide of type I collagen (ICTP), and C-terminal crosslinked telopeptide of type I collagen (CTX) were used as indicators to evaluate the resistance to enzymatic hydrolysis. Wight loss was further tested as macro-parameter to evaluate the effectiveness of aloin on dental collagen stability. Results : Aloin can interact with dental collagen via hydrogen bonds in humid environment. The interaction shorted the intermolecular distance and enhanced the proportion of stable α-helix chain of dental collagen. Aloin-treated dental collagen exhibited improved mechanical strength, thermostability and enzymatic stability. Conclusion : Aloin can strongly integrate with dental collagen under physiological conditions via hydrogen bonds and significantly enhance the dental collagen stability. Clinical Relevance : The use of aloin maybe a daily, nondestructive, and low-cost strategy to protect dental collagen. Thermostability ; Hydroxyproline ; Chemistry ; N-terminal telopeptide ; Thermogravimetric analysis ; Ultimate tensile strength ; Fourier transform infrared spectroscopy ; Type I collagen ; Hydrolysis ; Thermal stability ; Dentistry ; Materials science ; Enzyme ; Biochemistry ; Composite material ; Chemical engineering ; Organic chemistry ; Medicine ; Alkaline phosphatase Collagen: Extraction and Characterization
Authors: Св. С. Квон | V. Yu. Kulikov | S. K. Аrinova | Е. П. Щербакова
Source: CIS Iron and Steel Review (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Studying microstructure of heat-resistant alloy based on the Fe-Cr-Ni-alloying element system for manufacture of components for metallurgical equipment The paper presents the results of studying the microstructure and phase composition of an experimental heatresistant alloy based on the Fe-Cr-Ni-alloying element system intended for manufacturing of components for metallurgical equipment. Samples of an experimental alloy were prepared for research. Electron microscopy using X-ray mapping was used as a research method. X-ray phase analysis was performed to verify the phase identification. Influence of alloying elements on formation of intermetallic strengthening phases in an experimental alloy is considered. It is shown that the alloy matrix is represented by a &gamma;-solution by the type of substitution and interstitial phases of Ni3Al, MeC. The existence of a new intermetallic phase Me2B, identified taking into account the chemical composition of the alloy, as (Nb, Mo)2B, has been identified and experimentally proved. This phase is represented by significantly smaller inclusions. Microstructural and X-ray spectral analysis of the samples confirmed the presence of the above phases. It has been shown that the presence of this phase in the structure has a positive effect on the value of longterm strength. Furnace roller tables were cast from smelted experimental steel in normal operation. This research has been is funded by the Science&nbsp;Committee of the Ministry of Education and Science of the&nbsp;Republic of Kazakhstan AP09058350 &laquo;Developing and implementing&nbsp;the technology of producing chrome anti-friction&nbsp;cast iron for components of mining equipment&raquo;. Metallurgy ; Microstructure ; Alloy ; Materials science Metal Alloys Wear and Properties ; Material Properties and Applications ; Engineering and Environmental Studies
Authors: John Peter J. Nunez | Vaibhav Sharma | Jessika V. Rojas | Radhika Barua | Ravi L. Hadimani
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of X-Ray Irradiation on Magnetocaloric Material, (MnNiSi)1-x(Fe2Ge)x (MnNiSi)1-(Fe2Ge)x composition (x=0.34) alloy was prepared by arc melting, crushed, and sieved to approximately &lt;32 microns. They were utilized in examining the possible magnetic and structural changes when exposed to a dosage of a continuous sweeping rate of ~&gt;120 Gy/min and an absorbed dose of 35 kGy of X-ray radiation. This study reports observable trends in X-ray diffraction and magnetic measurements. Magnetization in the magnetization vs. temperature (both heating and cooling) measurements showed an increase from 2.72 emu/g to 4.01 emu/g in the irradiated sample. The Magnetization vs. magnetic field loops exhibited irradiation-induced magnetic hysteresis. The irradiated sample also exhibited an observable change in its coercivity of ΔHc = 14.7% at 200 K. A maximum entropy change ΔSmag of ~ 11.139 J/kg*K and a Tave peak of 317.5 K was achieved for the pristine sample in comparison to ΔSmag of ~ 11.349 J/kg*K and a Tave peak of 312.5 K for the irradiated sample. These presented results provided deeper insights into tuning the effect of irradiation to the magnetic properties of (MnNiSi)1-(Fe2Ge)x for composition (x=0.34) that can be utilized in a wide range of magnetocaloric applications in high-energy radiation environments. The irradiation applied to the sample did not induce any structural or magnetic phase changes in the selected composition but modified the magnetic properties marginally. Irradiation ; Magnetic refrigeration ; Magnetization ; Materials science ; Coercivity ; Magnetic hysteresis ; Analytical Chemistry (journal) ; Alloy ; X-ray ; Magnetic field ; Condensed matter physics ; Chemistry ; Optics ; Metallurgy ; Physics Magnetic and transport properties of perovskites and related materials ; Rare-earth and actinide compounds ; High Entropy Alloys Studies
Authors: Manna Dai | Yang Jiang | Feng Yang | Joyjit Chattoraj | Yingzhi Xia | Xinxing Xu | Weijiang Zhao | My Ha Dao | Yong Liu
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery. Metamaterial ; Computer science ; Constraint (computer-aided design) ; Deep learning ; Generative adversarial network ; Electronic engineering ; Artificial intelligence ; Physics ; Optics ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Animal Vocal Communication and Behavior
Authors: Joshua Ziegler | Florian Luthi | Mick Ramsey | Felix Borjans | Guoji Zheng | Justyna P. Zwolak
Source: arXiv (Cornell University) (preprint, 2023-01-20)
Field: Materials Science — Materials Chemistry

Automated extraction of capacitive coupling for quantum dot systems Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning to a regime suitable for qubit operations. Capacitive coupling ; Capacitive sensing ; Quantum dot ; Coupling (piping) ; Extraction (chemistry) ; Optoelectronics ; Materials science ; Physics ; Electrical engineering ; Engineering ; Mechanical engineering ; Chemistry ; Chromatography Quantum Dots Synthesis And Properties ; Molecular Junctions and Nanostructures ; Photonic and Optical Devices
Authors: В.Н. Стаценко | Л.Б. Леонтьев | Р.Х. Садыков
Source: МОРСКИЕ ИНТЕЛЛЕКТУАЛЬНЫЕ ТЕХНОЛОГИИ (article, 2023-03-04)
Field: Materials Science — General Materials Science

Research of film liquid neutralization of gas emissions from ship power plants Для снижения концентрации вредных газовых выбросов и аэрозолей судовых дизелей предлагается использование жидкостной нейтрализации. В процессе нейтрализации организовано пленочное течение жидкости по вертикальным пластинам. При движении выхлопных газов или загрязненного воздуха между пластинами пленка жидкости как абсорбент интенсивно поглощает газообразные и твердые загрязняющие вещества. На экспериментальном стенде проведено исследование интенсивности абсорбции в пленку воды углекислого газа. В экспериментах изменялись скорость газа, расход жидкости, концентрация вредных веществ в газе. В результате анализа полученных результатов выявлена оптимальная скорость газа, оптимальное значение орошения. При этом концентрацию двуокиси углерода возможно снизить на 30-45 % на длине контакта 0,6-1 м. Обобщение полученных результатов в широком диапазоне изменения концентраций газа, расходов жидкости и скорости движения загрязненного газа получено в виде зависимости между критериями Нуссельта, Рейнольдса и Прандтля. To reduce the concentration of harmful gas emissions and aerosols of marine diesel engines, the use of liquid neutralization is proposed. In the process of neutralization, a film flow of liquid over vertical plates is organized. When exhaust gases or polluted air move between the plates, the liquid film as an absorbent intensively absorbs gaseous and solid pollutants. On the experimental bench, the intensity of carbon dioxide absorption into the water film was studied. In the experiments, the gas velocity, liquid flow rate, and the concentration of harmful substances in the gas were changed. As a result of the analysis of the obtained results, the optimal gas velocity and the optimal irrigation value were revealed. At the same time, the concentration of carbon dioxide can be reduced by 30-45% at a contact length of 0.6-1 m. The generalization of the results obtained in a wide range of changes in gas concentrations, liquid flow rates, and the rate of movement of polluted gas is made in the form of a criterion dependence between the Nusselt, Reynolds and Prandtl criteria. Carbon dioxide ; Volumetric flow rate ; Absorption (acoustics) ; Diesel fuel ; Exhaust gas ; Prandtl number ; Range (aeronautics) ; Chemistry ; Materials science ; Environmental science ; Environmental engineering ; Mechanics ; Convection ; Composite material Material Properties and Applications ; Arctic and Russian Policy Studies ; Maritime Transport Emissions and Efficiency
Authors: Kwasi Nyandey | Daniel Jakubczyk
Source: arXiv (Cornell University) (preprint, 2023-07-08)
Field: Chemistry — Analytical Chemistry

Combining transmission speckle photography and convolutional neural network for determination of fat content in cow's milk -- an exercise in classification of parameters of a complex suspension We have combined transmission speckle photography and machine learning for direct classification and recognition of milk fat content classes. Our aim was hinged on the fact that parameters of scattering particles (and the dispersion medium) can be linked to the intensity distribution (speckle) observed when coherent light is transmitted through a scattering medium. For milk, it is primarily the size distribution and concentration of fat globules, which constitutes the total fat content. Consequently, we trained convolutional neural network to recognise and classify laser speckle from different fat content classes (0.5, 1.5, 2.0 and 3.2%). We investigated four exposure-time protocols and obtained the highest performance for shorter exposure times, in which the intensity histograms are kept similar for all images and the most probable intensity in the speckle pattern is close to zero. Our neural network was able to recognize the milk fat content classes unambiguously and we obtained the highest test and independent classification accuracies of 100 and ~99% respectively. It indicates that the parameters of other complex realistic suspensions could be classified with similar methods. Speckle pattern ; Globules of fat ; Intensity (physics) ; Histogram ; Transmission (telecommunications) ; Content (measure theory) ; Scattering ; Convolutional neural network ; Dispersion (optics) ; Artificial intelligence ; Suspension (topology) ; Pattern recognition (psychology) ; Photography ; Optics ; Mathematics ; Milk fat ; Physics ; Computer science ; Image (mathematics) ; Telecommunications ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Thermoregulation and physiological responses
Authors: M. HAJERA | M SANTHI | B. Divya
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

DETECTION OF CUMIN SEEDS ADULTERATION BASED ON THERMAL IMAGING USING DEEP LEARNING TECHNIQUE Food adulteration refers to the addition of artificial or poor-quality substances to a food product. Since the start of mankind, food adulteration has been a problem that artificial intelligence has successfully detected. In addition to lowering food quality, it also has several adverse health consequences. Two spices that are often used in the food industry are cumin and fennel, although they can be adulterated. Using deep learning, this research established a convolutional neural network (CNN) architecture that can identify between a food product and additional adulterants. This technique accurately detects 95.5% adulteration in cumin and fennel seeds. Key Words: Food adulteration, thermal imaging, spices, image classification, CNN, inception V3 algorithm. Convolutional neural network ; Artificial intelligence ; Food products ; Product (mathematics) ; Deep learning ; Quality (philosophy) ; Food quality ; Key (lock) ; Computer science ; Pattern recognition (psychology) ; Food science ; Mathematics ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Manna Dai
Source:  (preprint, 2023-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design &lt;p&gt;Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery.&lt;/p&gt; Metamaterial ; Computer science ; Constraint (computer-aided design) ; Deep learning ; Generative adversarial network ; Inverse ; Electronic engineering ; Artificial intelligence ; Optics ; Physics ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Millimeter-Wave Propagation and Modeling
Authors: Till Böhmer | Jan Philipp Gabriel | Costigliola, Lorenzo | Jan-Niklas Kociok | Tina Hecksher | Jeppe C. Dyre | Thomas Blochowicz
Source: arXiv (Cornell University) (preprint, 2023-12-04)
Field: Materials Science — Materials Chemistry

Time reversibility during the ageing of materials Physical ageing is the generic term for irreversible processes in glassy materials resulting from molecular rearrangements. One formalism for describing such ageing processes involves the concept of material time, which may be thought of as time measured on a clock whose rate changes as the glass ages. Experimental determination of material time has so far not been realized, however. Here, we show how dynamic light-scattering measurements provide a way forward. We determined the material time for an ageing sample of the glass-former 1-phenyl-1-propanol after temperature jumps close to the glass transition from the time-autocorrelation function of the intensity fluctuations probed by multispeckle dynamic light scattering. These fluctuations are shown to be stationary and reversible when regarded as a function of the material time. The glass-forming colloidal synthetic clay Laponite and a chemically ageing curing epoxy are also shown to display material-time-reversible scattered-light intensity fluctuations, and simulations of an ageing binary system monitoring the potential energy confirm material-time reversibility. In addition to demonstrating direct measurements of the material time, our findings identify a fundamental property of ageing in quite different contexts that presents a challenge to the current theories of ageing. Glass transition ; Autocorrelation ; Materials science ; Speckle pattern ; Chemical physics ; Composite material ; Statistical physics ; Thermodynamics ; Optics ; Chemistry ; Physics ; Polymer ; Mathematics Material Dynamics and Properties ; Glass properties and applications ; Liquid Crystal Research Advancements
Authors: Ming Zhang | Qi Meng | Deng Zhang | Yue Wang | Guanghui Wang | Zhiming Ma | Li Chen | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2023-05-26)
Field: Chemistry — Spectroscopy

Complex-valued neural operator assisted soliton identification The numerical determination of solitary states is an important topic for such research areas as Bose-Einstein condensates, nonlinear optics, plasma physics, etc. In this paper, we propose a data-driven approach for identifying solitons based on dynamical solutions of real-time differential equations. Our approach combines a machine-learning architecture called the complex-valued neural operator (CNO) with an energy-restricted gradient optimization. The former serves as a generalization of the traditional neural operator to the complex domain, and constructs a smooth mapping between the initial and final states; the latter facilitates the search for solitons by constraining the energy space. We concretely demonstrate this approach on the quasi-one-dimensional Bose-Einstein condensate with homogeneous and inhomogeneous nonlinearities. Our work offers a new idea for data-driven effective modeling and studies of solitary waves in nonlinear physical systems. Operator (biology) ; Generalization ; Artificial neural network ; Soliton ; Nonlinear system ; Einstein ; Physics ; Domain (mathematical analysis) ; Computer science ; Differential operator ; Dynamical systems theory ; Applied mathematics ; Classical mechanics ; Artificial intelligence ; Mathematical analysis ; Mathematics ; Quantum mechanics Spectroscopy and Laser Applications ; Meteorological Phenomena and Simulations ; Advanced Fiber Laser Technologies