jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lykourgos Chiniadis | Petros Tamvakis
Source: arXiv (Cornell University) (preprint, 2023-07-23)
Field: Chemistry — Analytical Chemistry

Rapid detection of soil carbonates by means of NIR spectroscopy, deep learning methods and phase quantification by powder Xray diffraction Soil NIR spectral absorbance/reflectance libraries are utilized towards improving agricultural production and analysis of soil properties which are key prerequisite for agroecological balance and environmental sustainability. Carbonates in particular, represent a soil property which is mostly affected even by mild, let alone extreme, changes of environmental conditions during climate change. In this study we propose a rapid and efficient way to predict carbonates content in soil by means of FT NIR reflectance spectroscopy and by use of deep learning methods. We exploited multiple machine learning methods, such as: 1) a MLP Regressor and 2) a CNN and compare their performance with other traditional ML algorithms such as PLSR, Cubist and SVM on the combined dataset of two NIR spectral libraries: KSSL (USDA), a dataset of soil samples reflectance spectra collected nationwide, and LUCAS TopSoil (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never before seen soil samples. Soil samples in KSSL and in TopSoil spectral libraries were acquired in the spectral region of visNIR, however in this study, only the NIR spectral region was utilized. Quantification of carbonates by means of Xray Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available. Up till now and to the best of our knowledge, there exists no other study, that presents a prediction model trained on such an extensive dataset with such promising results on unseen data, undoubtedly supporting the notion that deep learning models present excellent prediction tools for soil carbonates content. Topsoil ; Absorbance ; Soil test ; Environmental science ; European union ; Spectroscopy ; Soil science ; Random forest ; Near-infrared spectroscopy ; Soil water ; Chemistry ; Computer science ; Artificial intelligence ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Samuel G. McCallum | James E. Lerpiniére | Kjeld O. Jensen | Alison Walker
Source: arXiv (Cornell University) (preprint, 2023-09-21)
Field: Materials Science — Materials Chemistry

Bayesian optimisation approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are computationally intractable for a large number of input parameters, represented by a high-dimensional input space. It is therefore generally unclear as to whether a numerical simulation can reproduce a particular outcome (e.g. a set of experimental results) with a plausible set of model input parameters. Here, we present a method for efficiently searching the input space using Bayesian Optimisation to minimise the difference between the simulation output and a set of experimental results. Our method allows explicit evaluation of the probability that the simulation can reproduce the measured experimental results in the region of input space defined by the uncertainty in each input parameter. We apply this method to the simulation of charge-carrier dynamics in the perovskite semiconductor methyl-ammonium lead iodide MAPbI$_3$ that has attracted attention as a light harvesting material in solar cells. From our analysis we conclude that the formation of large polarons, quasiparticles created by the coupling of excess electrons or holes with ionic vibrations, cannot explain the experimentally observed temperature dependence of electron mobility. Parameter space ; Statistical physics ; Set (abstract data type) ; Bayesian probability ; Computer science ; Bayesian inference ; Uncertainty quantification ; Space (punctuation) ; Physics ; Mathematical optimization ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Mrinal Arandhara | Sai G. Ramesh
Source: ChemRxiv (preprint, 2023-10-25)
Field: Chemistry — Spectroscopy

Nuclear quantum effects in hydroxide hydrate along the H-bond bifurcation pathway Path integral simulations are used to explore nuclear quantum effects (NQEs) in hydroxide hydrate and its perdeuterated isotopomer along the H-bond bifurcation pathway. Towards this, a new PES using the symmetric gradient domain machine learning method with ab initio data at the CCSD(T)/aug-cc-pVTZ level is built. From path integral umbrella sampling simulations, free energy profiles along the bifurcation coordinate are explored as a function of temperature. At ambient temperature, the bifurcation barrier is increased upon the inclusion of NQEs. At low temperatures in the deep tunneling regime, the barrier is strongly decreased and flattened. These trends are examined and the role of the O-O distance is also investigated through two-dimensional umbrella sampling. Bifurcation ; Ab initio ; Path integral formulation ; Quantum ; Hydrate ; Chemistry ; Quantum tunnelling ; Physics ; Condensed matter physics ; Quantum mechanics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Ian Geiger | Diran Apelian | Xiaoqing Pan | Penghui Cao | Jian Luo | Timothy J. Rupert
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Frustrated metastable-to-equilibrium grain boundary structural transition in NbMoTaW due to segregation and chemical complexity Grain boundary structural transitions can lead to significant changes in the properties and performance of materials. In multi-principal element alloys, understanding these transitions becomes complex due to phenomena such as local chemical ordering and multi-component segregation. Using atomistic simulations, we explore a metastable-to-equilibrium grain boundary structural transition in NbMoTaW. The transition, characterized by structural disordering and reduced free volume, shows high sensitivity to its local chemical environment. Most notably, the transition temperature range of the alloy is more than twice that of a pure metal. Differences in composition between coexisting metastable and equilibrium structures highlight the change in local site availability due to structural relaxation. Further examination of grain boundaries with fixed chemical states at varying temperatures reveals that the amount of segregation significantly influences the onset temperature yet has minimal effect on the transition width. These insights underscore the profound effects of chemical complexity and ordering on grain boundary transitions in complex concentrated alloys, marking a meaningful advancement in our understanding of grain boundary behavior at the atomic level. Metastability ; Grain boundary ; Materials science ; Relaxation (psychology) ; Structural change ; Alloy ; Grain boundary strengthening ; Chemical physics ; Thermodynamics ; Condensed matter physics ; Metallurgy ; Microstructure ; Chemistry ; Physics Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; Advanced Materials Characterization Techniques
Authors: Ahmed Saleh | Ahmed Mahmood
Source: Al-Mağallaẗ al-ʻirāqiyyaẗ li-l-ṣaydalaẗ (article, 2023-05-29)
Field: Chemistry — Organic Chemistry

Docking Study for New 7-Aminocephalosporinic Acid Derivatives as Potential Inhibitors for β-Lactamases Background: Antibiotics with &beta;-Lactam rings (&beta;-Lactams), since they were discovered, have enhanced the typical treatment for bacterial infections. Though their resistance can quickly spread on a universal scale, bacterial resistance is primarily caused by the production of &beta;-lactamases. Hence, there is a serious demand to design and create new anti-&beta;-lactamases or inhibitors. Nowadays, the use of &beta;-lactamase inhibitors with &beta;-lactams reduces this resistance. Aim and Methods: This work aimed to help in silico design and dock two new series of 7-aminocephalosporinic acid derivatives (Schiff&rsquo;s bases and amides) against both the TEM-1 and the IMP-1 &beta;-lactamases. Results: The results revolve around the possible enhanced activity of eight amides and six Schiff base compounds compared with the standard inhibitors (clavulanic acid, sulbactam, and avabactam). These compounds show promising docking interactions with an active pocket site in both enzymes. Conclosin: We can conclude that both the halogenated and the hydrophobic substituents, alone or when containing oxygen atoms, will potentiate the affinity and the binding ability of any compounds when they are added to their structures to act as &beta;-lactamase inhibitors. Docking (animal) ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Biochemistry ; Pharmacology ; Computational biology ; Biology ; Medicine ; Veterinary medicine Synthesis of β-Lactam Compounds ; Phenothiazines and Benzothiazines Synthesis and Activities ; Synthesis and biological activity
Authors: Ahmed Saleh | Ahmed Mahmood
Source: Al-Mağallaẗ al-ʻirāqiyyaẗ li-l-ṣaydalaẗ (article, 2023-06-01)
Field: Chemistry — Organic Chemistry

Docking Study for New 7-Aminocephalosporinic Acid Derivatives as Potential Inhibitors for β-Lactamases Background: Antibiotics with &beta;-Lactam rings (&beta;-Lactams), since they were discovered, have enhanced the typical treatment for bacterial infections. Though their resistance can quickly spread on a universal scale, bacterial resistance is primarily caused by the production of &beta;-lactamases. Hence, there is a serious demand to design and create new anti-&beta;-lactamases or inhibitors. Nowadays, the use of &beta;-lactamase inhibitors with &beta;-lactams reduces this resistance. Aim and Methods: This work aimed to help in silico design and dock two new series of 7-aminocephalosporinic acid derivatives (Schiff&rsquo;s bases and amides) against both the TEM-1 and the IMP-1 &beta;-lactamases. Results: The results revolve around the possible enhanced activity of eight amides and six Schiff base compounds compared with the standard inhibitors (clavulanic acid, sulbactam, and avabactam). These compounds show promising docking interactions with an active pocket site in both enzymes. Conclosin: We can conclude that both the halogenated and the hydrophobic substituents, alone or when containing oxygen atoms, will potentiate the affinity and the binding ability of any compounds when they are added to their structures to act as &beta;-lactamase inhibitors. Docking (animal) ; Chemistry ; Pharmacology ; Combinatorial chemistry ; Stereochemistry ; Computational biology ; Biochemistry ; Biology ; Medicine Synthesis of β-Lactam Compounds ; Phenothiazines and Benzothiazines Synthesis and Activities ; Synthesis and biological activity
Authors: Calin-Andrei Pantis-Simut | Amanda Teodora Preda | L. Ion | Andrei Manolescu | George Alexandru Nemneş
Source: arXiv (Cornell University) (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Mapping confinement potentials and charge densities of interacting quantum systems using conditional generative adversarial networks Accurate and efficient tools for calculating the ground state properties of interacting quantum systems are essential in the design of nanoelectronic devices. The exact diagonalization method fully accounts for the Coulomb interaction beyond mean field approximations and it is regarded as the gold-standard for few electron systems. However, by increasing the number of instances to be solved, the computational costs become prohibitive and new approaches based on machine learning techniques can provide a significant reduction in computational time and resources, maintaining a reasonable accuracy. Here, we employ {\tt pix2pix}, a general-purpose image-to-image translation method based on conditional generative adversarial network (cGAN), for predicting ground state densities from randomly generated confinement potentials. Other mappings were also investigated, like potentials to non-interacting densities and the translation from non-interacting to interacting densities. The architecture of the cGAN was optimized with respect to the internal parameters of the generator and discriminator. Moreover, the inverse problem of finding the confinement potential given the interacting density can also be approached by the pix2pix mapping, which is an important step in finding near-optimal solutions for confinement potentials. Translation (biology) ; Discriminator ; Statistical physics ; Coulomb ; Generator (circuit theory) ; Computer science ; Generative grammar ; Physics ; Ground state ; Charge (physics) ; Quantum ; Inverse ; Artificial intelligence ; Algorithm ; Electron ; Mathematics ; Quantum mechanics ; Chemistry ; Geometry ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Image Processing Techniques and Applications
Authors: Yakun Zhang | Tingting Li | Libo Wang | Yalin Huang | Xingyang Yang | Hangxing Zhang | Gang Wang | Jinguang Li
Source: American journal of biochemistry & biotechnology/American journal of biochemistry and biotechnology (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Year Identification of Seeds in Peony (Paeonia suffruticosa Andr.) Using Hyperspectral Imaging Seed storage year is one of the important indicators for evaluating the quality of peony seeds. It is of great significance for the development of the peony industry to carry out rapid and non-destructive year identification of peony seeds to provide a basis for the screening of aged seeds during seed breeding and processing. This study explores the feasibility of using hyperspectral imaging technology combined with machine learning methods to identify the two states of peony seeds (shelled and non-shelled) and then determines the most suitable state for the year identification of peony seeds. The two states of peony seeds (shelled and non-shelled) in 2017, 2018, and 2019 are employed as the research objects. Hyperspectral imaging data of two kinds of peony seeds in the spectral range of 935-1720 nm are collected. The machine learning methods based on the two states of peony seeds (shelled and non-shelled), including partial least squares (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classification models, are established and compared. It is found that the optimal year identification models of peony seeds (shelled and non-shelled) based on hyperspectral imaging technology have better recognition effects and the recognition accuracy is more than 99.5%. Moreover, the recognition accuracy of the year identification PLS-DA model established by non-shelled peony seeds is 99.96%, which is better than that of shelled peony seeds at 99.64%. This indicates that year identification of peony seeds based on hyperspectral imaging technology is feasible and efficient and that non-shelled peony seeds are more suitable for the year identification of peony seeds. The results can provide a theoretical and methodological justification for the screening of high-quality peony seeds. Hyperspectral imaging ; Identification (biology) ; Artificial intelligence ; Paeonia suffruticosa ; Convolutional neural network ; Plant identification ; Pattern recognition (psychology) ; Support vector machine ; Mathematics ; Computer science ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Nanoscale, 2023, 15, 9233 DOI: 10.1039/D3NR90102E This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Computer science ; Chromatin structure remodeling (RSC) complex ; World Wide Web ; Library science ; Computer security ; Chemistry ; Psychology ; Social psychology Quantum Dots Synthesis And Properties ; Advanced Nanomaterials in Catalysis
Authors: Mehmet Akif Erden | Rajab Elkilani | Harun Çuğ
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

THE INFLUENCE OF HEAT TREATMENT ON MICROSTRUCTURE AND TRIBOLOGICAL PROPERTIES OF PM 316L STAINLESS STEEL CONTAINING 7% Si Powder metallurgy (PM) has several technological applications in the automotive and aerospace industries, making it important for modern manufacturing. PM is needed in manufacturing for high productivity, geometric precision, surface quality, and material efficiency. Due to their wear and corrosion resistance and high strength, austenitic steels like SS316L are used in the aerospace, medical, automotive, and marine industries to make complex and custom parts. In this study, the microstructure and tribological characterization of PM-heat-treated SS316L+7Si were examined. Microstructure ; Tribology ; Aerospace ; Materials science ; Metallurgy ; Automotive industry ; Austenite ; Corrosion ; Powder metallurgy ; Wear resistance ; Engineering Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials ; Aluminum Alloys Composites Properties
Authors: Matteo Springolo | Miquel Royo | Massimiliano Stengel
Source: arXiv (Cornell University) (preprint, 2023-03-31)
Field: Materials Science — Materials Chemistry

In-plane flexoelectricity in two-dimensional $D_{3d}$ crystals We predict a large in-plane polarization response to bending in a broad class of trigonal two-dimensional crystals. We define and compute the relevant flexoelectric coefficients from first principles as linear-response properties of the undistorted layer, by using the primitive crystal cell. The ensuing response (evaluated for SnS$_{2}$, silicene, phosphorene and RhI$_{3}$ monolayers and for a hexagonal BN bilayer) is up to one order of magnitude larger than the out-of-plane components in the same material. We illustrate the topological implications of our findings by calculating the polarization textures that are associated with a variety of rippled and bent structures. We also determine the longitudinal electric fields induced by a flexural phonon at leading order in amplitude and momentum. Bent molecular geometry ; Flexoelectricity ; Condensed matter physics ; Silicene ; Polarization (electrochemistry) ; Trigonal crystal system ; Phosphorene ; Phonon ; Topological defect ; Amplitude ; Polarization density ; Materials science ; Liquid crystal ; Physics ; Optics ; Crystallography ; Crystal structure ; Nanotechnology ; Chemistry ; Graphene ; Quantum mechanics Nonlocal and gradient elasticity in micro/nano structures ; Mechanical and Optical Resonators ; Quantum-Dot Cellular Automata
Authors: Alfredo Fiorentino | Paolo Pegolo | Stefano Baroni
Source: arXiv (Cornell University) (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Hydrodynamic finite-size scaling of the thermal conductivity in glasses In the past few years, the theory of thermal transport in amorphous solids has been substantially extended beyond the Allen-Feldman model. The resulting formulation, based on the Green-Kubo linear response or the Wigner-transport equation, bridges this model for glasses with the traditional Boltzmann kinetic approach for crystals. The computational effort required by these methods usually scales as the cube of the number of atoms, thus severely limiting the size range of computationally affordable glass models. Leveraging hydrodynamic arguments, we show how this issue can be overcome through a simple formula to extrapolate a reliable estimate of the bulk thermal conductivity of glasses from finite models of moderate size. We showcase our findings for realistic models of paradigmatic glassy materials. Thermal conductivity ; Statistical physics ; Scaling ; Limiting ; Range (aeronautics) ; Boltzmann equation ; Simple (philosophy) ; Thermal ; Boltzmann constant ; Kinetic energy ; Amorphous solid ; Cube (algebra) ; Kinetic theory ; Physics ; Materials science ; Mathematics ; Thermodynamics ; Classical mechanics ; Chemistry ; Geometry Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Feng Huang | Xingyu Wang | Qian Hu | Jiawei Zhang | Jing Liu
Source: Research Square (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Galvanic corrosion behavior of bainite weathering steel welded joint in simulated marine atmosphere environment based on capillary microcell Abstract The kinetical behavior and mechanism of micro-galvanic corrosion of Q690 bainitic weathering steel welded joint in marine atmosphere were investigated. The results showed that a micro-galvanic corrosion cell formed among different regions of the Q690 welded joint. The kinetical behavior of micro-galvanic corrosion could be divided into four stages: the initiation and development of micro-galvanic corrosion, the decreasing of galvanic corrosion rate, the polarity reversal of galvanic couple, and the disappearance of galvanic effect. The difference of corrosion rate between Q690 steel and its welded joint is closely related to the micro-galvanic corrosion kinetic behavior. Galvanic cell ; Corrosion ; Materials science ; Galvanic corrosion ; Metallurgy ; Welding ; Galvanic anode ; Cathodic protection ; Electrochemistry ; Electrode ; Chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Media Ismael Sulaiman
Source: Journal of Zankoy Sulaimani - Part A (article, 2023-06-20)
Field: Chemistry — Spectroscopy

Vibrational frequencies of entrance and exit channels of CH4 with the radical Cl atom in the van der Waals region Vibrational frequencies in the C-H stretching region, equilibrium structures, and the minimum energy of entrance and exit channels of radical chlorine attached to the methane molecule via van der Waals bonds are predicted via ab initio calculations. MP2 theory at the AUG-cc-pVDZ basis set is used for the calculations. Two equilibrium structures at minimum energies are predicted. A radical Cl atom attached by van der Waals bonds to a specific H atom in the CH 4 molecules in one structure. This structure has two bands of C-H vibration frequencies at 3212 cm-1 and 3222 cm -1 that are higher than the C-H vibration of pure CH4. In the second structure at the entrance channel, the chlorine atom connects to a face (tetrahedron) of CH4 by a van der Waals bond that has a deeper minimum of energy than the first structure. The rear structure has two C-H stretches of 3197 cm -1 and 3199 cm -1. Finally, the minimum energy is predicted for both the first and second structures in the entrance channel, with relative energies of -2.2 kJ mol-1 and -4.6 kJ mol-1, respectively. The structure of the exit channel shows a relative minimum energy of 25.7 kJ mol-1. Our results of the IR spectrum of harmonic vibrational frequencies are original. van der Waals force ; Atomic physics ; Atom (system on chip) ; Van der Waals strain ; Chemistry ; Van der Waals radius ; Physics ; Molecular physics ; Quantum mechanics ; Molecule ; Engineering Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Laser Applications