jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yannick Couzinié | Yusuke Nishiya | Hirofumi Nishi | Taichi Kosugi | Hidetoshi Nishimori | Yu‐ichiro Matsushita
Source: arXiv (Cornell University) (preprint, 2023-07-06)
Field: Materials Science — Materials Chemistry

Annealing for prediction of grand canonical crystal structures: Efficient implementation of n-body atomic interactions We propose an annealing scheme usable on modern Ising machines for crystal structures prediction (CSP) by taking into account the general n-body atomic interactions, and in particular three-body interactions which are necessary to simulate covalent bonds. The crystal structure is represented by discretizing a unit cell and placing binary variables which express the existence or non-existence of an atom on every grid point. The resulting quadratic unconstrained binary optimization (QUBO) or higher-order unconstrained binary optimization (HUBO) problems implement the CSP problem and is solved using simulated and quantum annealing. Using the example of Lennard-Jones clusters we show that it is not necessary to include the target atom number in the formulation allowing for simultaneous optimization of both the particle density and the configuration and argue that this is advantageous for use on annealing machines as it reduces the total amount of interactions. We further provide a scheme that allows for reduction of higher-order interaction terms that is inspired by the underlying physics. We show for a covalently bonded monolayer MoS2 crystal that we can simultaneously optimize for the particle density as well as the crystal structure using simulated annealing. We also show that we reproduce ground states of the interatomic potential with high probability that are not represented on the initial discretization of the unit cell. Quadratic unconstrained binary optimization ; Crystal structure prediction ; Simulated annealing ; Quantum annealing ; Binary number ; Discretization ; Statistical physics ; Molecular dynamics ; Quadratic equation ; Hamiltonian (control theory) ; Computer science ; Crystal structure ; Materials science ; Mathematics ; Quantum ; Physics ; Chemistry ; Algorithm ; Mathematical optimization ; Computational chemistry ; Quantum computer ; Quantum mechanics ; Crystallography ; Mathematical analysis Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: H. Vimalashree | A. Sathish | C. T. Subbarayappa | S. Dharumarajan | Premanand B. Dashavant | J. Venkate Gowda
Source: International Journal of Environment and Climate Change (article, 2023-12-06)
Field: Chemistry — Analytical Chemistry

Variations in Soil Texture Using Machine Learning Models Based on the Spectral Response of Soil Samples in the Visible-Near-Infrared (Vis-NIR) Region In this study, we systematically assessed the predictive performance of two machine learning models; Random Forest (RF) and Partial Least Squares Regression (PLSR), utilizing various pre-processing techniques for the determination of soil properties, including sand, silt and clay fractions. The evaluation was conducted based on key performance metrics, with the following values recorded: RF exhibited outstanding results with high R2 values (0.88 for calibration and 0.55 for validation) and low RMSE (0.48 for calibration and 1.81 for validation), especially excelling in predicting clay content (R2: 0.92 in calibration and 0.78 in validation). Moreover, the RF model demonstrated impressive RPD (12.5 in calibration and 4.55 in validation) and RPIQ (4.87 in calibration and 4.13 in validation) values for clay. PLSR demonstrated moderate performance, achieving acceptable R2 values for sand, silt and clay fractions, with the highest R2 value of 0.79 achieved in sand content prediction using MSC pre-processing. The RPD and RPIQ scores supported the model's reliability. These findings offer valuable guidance for selecting suitable models and pre-processing techniques for soil property prediction, with Random Forest emerging as the top choice for accurate and reliable results. Silt ; Calibration ; Partial least squares regression ; Random forest ; Soil texture ; Soil test ; Reliability (semiconductor) ; Predictive modelling ; Soil science ; Environmental science ; Remote sensing ; Artificial intelligence ; Machine learning ; Computer science ; Mathematics ; Statistics ; Geology ; Soil water ; Physics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Smart Agriculture and AI
Authors: Brian R. Pauw | Sofya Laskina | Aakash Ashok Naik | Glen J. Smales | Janine George | Ingo Breßler | Philipp Benner
Source: arXiv (Cornell University) (preprint, 2023-03-23)
Field: Materials Science — Materials Chemistry

"Ultima Ratio": Simulating wide-range X-ray scattering and diffraction We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity. The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q &lt; 0.01 1/nm up to Q &lt; 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts. Scattering ; Small-angle X-ray scattering ; Diffraction ; Materials science ; Range (aeronautics) ; Fast Fourier transform ; Computation ; Computational physics ; Optics ; Intensity (physics) ; Fourier transform ; Crystallinity ; Pair distribution function ; Physics ; Computer science ; Algorithm X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Laureen Coïc | Raffaele Vitale | Myriam Moreau | David Rousseau | José Henrique de Morais Goulart | Nicolas Dobigeon | Cyril Ruckebusch
Source: ChemRxiv (preprint, 2023-07-09)
Field: Chemistry — Analytical Chemistry

Assessment of essential information in the Fourier domain to accelerate Raman hyperspectral microimaging In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows or/and columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on principal component analysis (PCA) to evaluate the convex hull that encapsulates the data structure in the normalized scores space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition. Hyperspectral imaging ; Principal component analysis ; Fourier transform ; Context (archaeology) ; Computer science ; Phasor ; Convex hull ; Raman spectroscopy ; Partial least squares regression ; Artificial intelligence ; Remote sensing ; Mathematics ; Optics ; Physics ; Regular polygon ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Imaging and Spectroscopy Techniques
Authors: Jiayuan Tang
Source: Academic Journal of Computing & Information Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Identification of Cultivated and Wild Mushrooms Using Laser-Induced Breakdown Spectroscopy Technology Combined with Machine Learning Algorithms This study aimed to identify the cultivated and wild growth modes of mushrooms using Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning algorithms. A total of 230 mushroom slice samples were collected, and their LIBS spectral data were analyzed. Multiple machine learning algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were employed for classification. The results showed that the integration of Principal Component Analysis (PCA) significantly reduced the modeling analysis time for MLP, SVM, and KNN. Among the six classification methods, PCA+KNN achieved the highest average accuracy of 94.7%. This indicates the feasibility of using LIBS combined with machine learning algorithms for distinguishing different types of mushroom samples. The innovation of this study lies in the utilization of PCA for dimensionality reduction and the combination of various machine learning algorithms for mushroom sample classification, leading to improved accuracy and modeling speed. Identification (biology) ; Laser-induced breakdown spectroscopy ; Spectroscopy ; Algorithm ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Physics ; Biology ; Botany ; Astronomy Spectroscopy and Chemometric Analyses ; Silymarin and Mushroom Poisoning ; Fermentation and Sensory Analysis
Authors: Owen Watts Moore | Conor M. Lewis | Thomas Ross | Thomas Andrew Waigh | Nickolay Korabel | César Mendoza
Source: arXiv (Cornell University) (preprint, 2023-11-20)
Field: Chemistry — Organic Chemistry

Extreme heterogeneity in the microrheology of lamellar surfactant gels analyzed with neural networks The heterogeneity of the viscoelasticity of a lamellar gel network based on cetyl-trimethylammonium chloride (CTAC) and ceto-stearyl alcohol was studied using particle tracking microrheology. A recurrent neural network (RNN) architecture was used for estimating the Hurst exponent, $H$, on small sections of tracks of probe spheres moving with fractional Brownian motion. Thus dynamic segmentation of tracks via neural networks was used in microrheology for the first time and it is significantly more accurate than using mean square displacements. An ensemble of 414 particles produces a mean squared displacement (MSD) that is subdiffusive in time, $t$, with a power law of the form $t^{0.74\pm0.02}$, indicating power law viscoelasticity. RNN analysis of the probability distributions of $H$, combined with detailed analysis of the time-averaged MSDs of individual tracks, revealed diverse diffusion processes belied by the simple scaling of the ensemble MSD, such as caging phenomena, which give rise to the complex viscoelasticity of lamellar gels. Microrheology ; Viscoelasticity ; Brownian motion ; Power law ; Lamellar structure ; Mean squared displacement ; Materials science ; Statistical physics ; Physics ; Mathematics ; Thermodynamics ; Statistics ; Composite material Surfactants and Colloidal Systems
Authors: Kapildeb Dolui | Lewis J. Conway | Christoph Heil | Timothy A. Strobel | Rohit P. Prasankumar | Chris J. Pickard
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

Feasible route to high-temperature ambient-pressure hydride superconductivity A key challenge in materials discovery is to find high-temperature superconductors. Hydrogen and hydride materials have long been considered promising materials displaying conventional phonon-mediated superconductivity. However, the high pressures required to stabilize these materials have restricted their application. Here, we present results from high-throughput computation, considering a wide range of high-symmetry ternary hydrides from across the periodic table at ambient pressure. This large composition space is then reduced by considering thermodynamic, dynamic, and magnetic stability, before direct estimations of the superconducting critical temperature. This approach has revealed a metastable ambient-pressure hydride superconductor, Mg$_2$IrH$_6$, with a predicted critical temperature of 160 K, comparable to the highest temperature superconducting cuprates. We propose a synthesis route via a structurally related insulator, Mg$_2$IrH$_7$, which is thermodynamically stable above 15 GPa and discuss the potential challenges in doing so. Ambient pressure ; Superconductivity ; Hydride ; Metastability ; Ternary operation ; Condensed matter physics ; High-temperature superconductivity ; Materials science ; Hydrogen ; Thermodynamics ; Physics ; Quantum mechanics ; Computer science Hydrogen Storage and Materials ; High-pressure geophysics and materials ; Superconductivity in MgB2 and Alloys
Authors: Mustafa Çiçekler | Ahmet Tutuş
Source:  (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

A Mini Review: Harnessing Artificial Intelligence for a Sustainable and Competitive Pulp and Paper Industry The pulp and paper industry is undergoing a transformation as it seeks to maintain competitiveness and sustainability in the face of evolving market demands and environmental concerns. Harnessing Artificial Intelligence (AI) for a Sustainable and Competitive Pulp and Paper Industry explores the potential of AI technologies to revolutionize various aspects of the sector, from optimizing production processes to enhancing product quality and reducing waste. This paper delves into the challenges and opportunities associated with AI implementation, including data quality and availability, integration with existing systems, workforce adaptation, and ethical considerations. By addressing these challenges and fostering collaboration between industry stakeholders, technology providers, researchers, and governments, the pulp and paper industry can successfully integrate AI into their operations and reap the benefits of improved efficiency, cost savings, and environmental sustainability. Through a commitment to innovation and collaboration, the pulp and paper industry can harness the power of AI to drive a more sustainable and competitive future. Sustainability ; Workforce ; Industry 4.0 ; Business ; Competitive advantage ; Industrial organization ; Computer science ; Marketing ; Economics Wood and Agarwood Research
Authors: Jianzhi Xu | Joost M. Bakker | Olga V. Lushchikova | Peter Lievens | Ewald Janssens | Gao‐Lei Hou
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Chemistry — Organic Chemistry

Pentagon, Hexagon, or Bridge? Identifying the Location of a Single Vanadium Cation on Buckminsterfullerene Surface Buckminsterfullerene C60 has received extensive research interest ever since its discovery. In addition to its interesting intrinsic properties of exceptional stability and electron-accepting ability, the broad chemical tunability by decoration or substitution on the C60-fullerene surface makes it a fascinating molecule. However, to date there is uncertainty about the binding location of such decorations on the C60 surface, even for a single adsorbed metal atom. In this work, we report the gas-phase synthesis of the C60V+ complex and its in-situ characterization by mass spectrometry and in-frared spectroscopy with the help of quantum chemical calculations and molecular dynamics simulations. We identify the most probable binding position of a vanadium cation on C60 above a pentagon center in eta5-fashion, demonstrate a high thermal stability for this complex, and explore the bonding nature between C60 and the vanadium cation, reveal-ing that large orbital and electrostatic interactions lie at the origin of the stability of the eta5-C60V+ complex. Buckminsterfullerene ; Vanadium ; Chemical physics ; Fullerene ; Molecule ; Thermal stability ; Mass spectrometry ; Atom (system on chip) ; Chemistry ; Pentagon ; Crystallography ; Materials science ; Computational chemistry ; Inorganic chemistry ; Organic chemistry Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies ; Diamond and Carbon-based Materials Research
Authors: Fenghui Guo | Wei Xu | Minghui Tang | Ziqi Zhang | Zean Chen
Source: Research Square (preprint, 2023-11-20)
Field: Materials Science — Biomaterials

Investigation of the Mineralogical Composition and Origin Analysis of Black Jadeite Abstract Analyzing black jadeite variety not only helps to distinguish it from the more common omphacite jadeite, but also helps to study the origin of jadeite by studying these special specimens. The basic gemological properties, mineral composition, structural characteristics, spectroscopic features, and color-causing mechanisms of black jadeite were studied through testing methods such as Polarizing microscope, X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy. Research has shown that black Jadeite have columnar crystalloblastic texture, granular crystalloblastic texture, fibrous crystalloblastic texture, and fracture structures. The secondary minerals are black impurities, manifested as opaque irregular small dots, and their Raman spectra and X-ray diffraction show typical characteristics of a mixture of graphite and disordered graphite. Based on the distribution of graphite and disordered graphite mixtures, they exhibit varying degrees of light absorption, resulting in the formation of different shades of black, which is the color-causing factor of black jadeite. These carbonaceous substances are the result of the carbonization of mixed organic matter in the fluid during the metamorphic process, gradually forming dispersed graphite and disordered graphite, distributed in mineral crystals, the interstices between mineral crystals, mineral crystal fracture surfaces, and cataclastic texture, indicating a multi-stage metamorphic cause. The discovery of graphite in jadeite supports the hypothesis that jadeite protolith originated from the subduction process of the Neotethys Ocean, accompanied by the metasomatism and high-pressure metamorphism of olivinite. Graphite ; Omphacite ; Mineral ; Raman spectroscopy ; Materials science ; Mineralogy ; Texture (cosmology) ; Crystallography ; Analytical Chemistry (journal) ; Geology ; Chemistry ; Composite material ; Metallurgy Clay minerals and soil interactions ; Mineralogy and Gemology Studies ; Therapeutic Uses of Natural Elements
Authors: Ahmet ÖZBAYRAK
Source: Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

Calculation of buckling loads of ipe-section bending members based on optimization of analytical formulation The critical lateral buckling load of cantilever beams with IPE cross-section was calculated using analytical closed-form equations and numerical finite element analyses within the scope of the research.The equations suggested in the specifications for simply supported beams were used to calculate the buckling load of cantilever beams.The rationality of the values calculated due to this is not fully known.In the research, a single loading was made to the shear center at the free end of the cantilever beam.Cantilever length and section height were kept variable.As a result, it has been determined that there are partial differences in the analysis result obtained from the elastic stability theory and finite element method.Accordingly, the results obtained from ANSYS and SAP2000 analyses confirm each other.On the other hand, the results obtained using the formulation of Timoshenko and Gere, the calculation results made according to the AISC and DCCPSS regulations, and the results obtained from the LTBeam program confirm each other.However, it differs from the FEA analysis due to the cantilever beam length's shortening and the section height increase.Thus, to obtain accurate and reliable results in the buckling load calculation of cantilever beams, the equations used in analytical calculations were optimized according to finite element analysis (FEA) results.As a result of the study conducted according to the error criteria, it was determined that the updated equation results gave similar results to the FEA results. Computer science Structural mechanics and materials ; Topology Optimization in Engineering ; Composite Structure Analysis and Optimization
Authors: Rajkumar Sarma | Steffen Hardt
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation The thermoelectric response of thermally activated electrolytes (TAE) in a slit channel is studied theoretically and by numerical simulations. The term TAE refers to electrolytes whose charge carrier concentration is a function of temperature, as recently suggested for ionic liquids and highly concentrated aqueous electrolyte solutions. Two competing mechanisms driving charge transport by temperature gradients are identified. For suitable values of the activation energy that governs the generation of charge carriers, a giant thermoelectric response is found, which could help explaining recent experimental results for nanoporous media infiltrated with TAEs. Electrolyte ; Thermoelectric effect ; Charge carrier ; Materials science ; Charge (physics) ; Nanoporous ; Chemical physics ; Ionic bonding ; Activation energy ; Ionic conductivity ; Ion ; Chemistry ; Nanotechnology ; Optoelectronics ; Thermodynamics ; Physics ; Electrode ; Physical chemistry Advanced Thermoelectric Materials and Devices ; Advanced Battery Materials and Technologies ; Spectroscopy and Quantum Chemical Studies
Authors: Wei Liu | Meng Li | Houqiang Teng | H.Y. Liu | Zhi Li | Yu Niu | Ziren Luo
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Chemistry — Electrochemistry

Direct imaging of single-molecule electronic states in chemical reactions Current single-molecule imaging techniques are incapable of providing stochastic information of individual chemical reactions. We have developed an optical imaging method for stochastic analysis of individual electrochemical reactions. The method bypasses diffraction limits and allows for the direct imaging of the electronic states of single molecules in electrochemical reactions. It involves using surface plasmon resonance and electrode potential modulation to reduce electron density background on macroelectrode surfaces, thus making it possible to measure individual redox reactions, identify different redox states of single molecules, and analyze reaction probability fluctuations under different conditions. The technique enables the acquisition of electron transfer and stochastic information in chemical processes, providing unique insights into single-molecule reactions with broad applications in biology, materials, computational science, and other scientific communities. Redox ; Molecule ; Electrochemistry ; Chemical physics ; Chemical reaction ; Surface plasmon resonance ; Electrode ; Plasmon ; Chemistry ; Nanotechnology ; Materials science ; Physical chemistry ; Optoelectronics ; Nanoparticle Electrochemical Analysis and Applications ; Advanced Fluorescence Microscopy Techniques ; Molecular Junctions and Nanostructures