jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arjun B. Savel | Megan Bedell | Eliza M.-R. Kempton
Source: The Journal of Open Source Software (article, 2024-08-10)
Field: Chemistry — Spectroscopy

cortecs: A Python package for compressingopacities The absorption and emission of light by exoplanet atmospheres encode details of atmospheric composition, temperature, and dynamics.Fundamentally, simulating these processes requires detailed knowledge of the opacity of gases within an atmosphere.When modeling broad wavelength ranges at high resolution, such opacity data, for even a single gas, can take up multiple gigabytes of system random-access memory (RAM).This aspect can be a limiting factor when considering the number of gases to include in a simulation, the sampling strategy used for inference, or even the architecture of the system used for calculations.Here, we present cortecs, a Python tool for compressing opacity data.cortecs provides flexible methods for fitting the temperature, pressure, and wavelength dependencies of opacity data and for evaluating the opacity with accelerated, GPU-friendly methods.The package is actively developed on GitHub (https://github.com/arjunsavel/cortecs),and it is available for download with pip and conda. Statement of needObservations with the latest high-resolution spectrographs (e.g., IGRINS / Gemini South, ESPRESSO / VLT, MAROON-X / Gemini North; Mace et al. ( 2018); Seifahrt et al. (2020); Pepe et al. ( 2021)) have motivated RAM-intensive simulations of exoplanet atmospheres at high spectral resolution.cortecs enables these simulations with more gases and on a broader range of computing architectures by compressing opacity data. Opacity ; Python (programming language) ; Exoplanet ; Computer science ; Computational science ; Physics ; Operating system ; Optics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Stellar, planetary, and galactic studies
Authors: Yueran Wang | Peter Harrowell
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Materials Science — Materials Chemistry

Structural Diversity in Condensed Matter: A General Characterization of Crystals, Amorphous Solids and the Structures Between A definition of structural diversity, adapted from the biodiversity literature, is introduced to provide a general characterization of structures of condensed matter. Using the Favored Local Structure (FLS) lattice model as a testbed, the diversity measure is found to effectively filter extrinsic noise and to provide a useful differentiation between crystal and amorphous structures. We identify an interesting class of structures intermediate between crystals and glasses that are characterised by a complex combination of short-range ordering and long-range disorder. We demonstrate how the diversity can be used as an order parameter to organise various scenarios by structure change in response to increasing diversity. Characterization (materials science) ; Amorphous solid ; Diversity (politics) ; Materials science ; Chemical physics ; Crystallography ; Nanotechnology ; Physics ; Chemistry ; Political science ; Law Material Dynamics and Properties ; Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography
Authors: Fabricio Varela Marín Alvaro | Timur Aygumov | Movsar Musaevich Matygov
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Training of a Convolutional Neural Network for the Classification of Coffee Fruit State Convolutional neural networks are recognized for their high artificial intelligence capacity, mostly for their identification of objects either in images or videos. Honduras has a strong dependence on coffee cultivation, as it plays a crucial role in both its culture and economy. However, Honduras has endured difficulties linked to aspects such as quality, contamination, processing problems, illnesses, or lack of training. The root of problems often arises during the coffee harvest, and since Honduras is a less developed country, the use of advanced technology is not common. The recurring research aims to train a cnn model by using images and object detection to classify the quality of the coffee fruit. The training of a convolutional neural network was developed by using Robo Flow for the classification of the state of the coffee fruit. Firstly, the coffee farm was visited to see firsthand the fruit in its various stages. Due to the seasonal cuts established on the farms, a second visit was made to collect the coffee fruit. After that, the fruit was taken into a controlled environment to start the photo session. The session concluded with a total of 1702 photographs. The fruits within the images were individually annotated by classifying them into two categories: good and bad. A model result gave a precision, recall and mpa of 97.7%, 94.1% and 98.3%, respectively. The configurations were the use of 600 images, using a ratio of 80:10:10 for the division of training, validation, and testing tasks. Only one adjustment was made in the preprocessing, which consisted of changing the size of the images from 3000x4000 to 640x640. This is considered the best result after carrying out more training to test the alteration of the results of other models by making changes such as the number of images and the types of augmentations. Therefore, the results conclude the fulfillment of the objective. Convolutional neural network ; Artificial intelligence ; Training (meteorology) ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Yogita Sharma
Source: Universal Research Reports (article, 2024-07-17)
Field: Chemistry — Organic Chemistry

One pot facile synthesis of flavanoidal oxadiazinanones: In vitro antibacterial activity, docking and MD simulation using DNA gyrase Flavanones are commonly spread in nature because they are essential intermediates in the biosynthetic flavonoid pathway1 and are of therapeutic significance because of their broad variety of biological activities such as hypertensive, antifungal, antibacterial, and antitumor activities2. Therefore, the field of flavanones is of numerous attentions to medicinal chemists for drug discovery. Flavanones have a 2,3- dihydro skeleton in the C6-C3-C6 structure of the flavonoid and do not have a double bond between C2 and C3, (Fig. 1) which renders them chiral in the C2 position3 hence two stereoisomeric forms of each flavanone are possible4. The chirality means that the B-ring is distorted compared to the A-C rings and is not planar. Such a disparity in molecular orientation is of considerable importance as it may influence how flavonoids engage with biological receptors and their bioactive properties5,6. DNA gyrase ; Docking (animal) ; In vitro ; Chemistry ; Antibacterial activity ; Combinatorial chemistry ; Biology ; Biochemistry ; Escherichia coli ; Medicine ; Genetics ; Bacteria Synthesis and biological activity
Authors: Yunting Li | Xiaopeng Cui | Zhaoping Xiong | Zuoheng Zou | Bowen Liu | Bi-Ying Wang | Runqiu Shu | Huangjun Zhu | Nan Qiao | Man‐Hong Yung
Source: arXiv (Cornell University) (preprint, 2024-04-22)
Field: Chemistry — Spectroscopy

Efficient molecular conformation generation with quantum-inspired algorithm Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature. Algorithm ; Quantum ; Computer science ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Various Chemistry Research Topics
Authors: Brieuc Le Dé | Simon Huppert | Riccardo Spezia | Alex W. Chin
Source: arXiv (Cornell University) (preprint, 2024-05-14)
Field: Chemistry — Physical and Theoretical Chemistry

Extending Non-Perturbative Simulation Techniques for Open-Quantum Systems to Excited-State Proton Transfer and Ultrafast Non-Adiabatic Dynamics Excited state proton transfer is an ubiquitous phenomenon in biology and chemistry, spanning from the ultrafast reactions of photo-bases and acids to light-driven, enzymatic catalysis and photosynthesis. However, the simulation of such dynamics involves multiple challenges, since high-dimensional, out-of-equilibrium vibronic states play a crucial role, while a fully quantum description of the proton's dissipative, real-space dynamics is also required. In this work, we extend the powerful Matrix Product State approach to open quantum systems (TEDOPA) to study these demanding dynamics, and also more general non-adiabatic processes that can appear in complex photochemistry subject to strong laser driving. As an illustration, we initially consider an open model of a four-level electronic system interacting with hundreds of intramolecular vibrations that drive ultrafast excited state proton transfer, as well as an explicit photonic environment that allows us to directly monitor the resulting dual fluorescence in this system. We then demonstrate how to include a continuous 'reaction coordinate' of the proton transfer that allows numerically exact simulations that can be understood, visualized and interpreted in the familiar language of diabatic and adiabatic dynamics on potential surfaces, while also retaining an exact quantum treatment of dissipation and driving effects that could be used to study diverse problems in ultrafast photochemistry. Adiabatic process ; Excited state ; Ultrashort pulse ; Quantum ; Physics ; Proton ; Dynamics (music) ; Quantum mechanics ; Quantum dynamics ; Statistical physics Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Quantum optics and atomic interactions
Authors: Umesha D. K
Source: Journal of Electrical Systems (article, 2024-11-16)
Field: Chemistry — Analytical Chemistry

Segmentation of Arecanut Bunches Using Deep Learning Technique Image segmentation plays a crucial role in computer vision, enabling the extraction of detailed features from images for a wide range of applications. Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in achieving accurate segmentation outcomes across various domains. This study presents a novel method for segmenting unharvested arecanut bunch images using CNN models. An optimized U-Net model was utilized for segmentation, and a comparative analysis was conducted across three different color spaces: RGB, saturation, and grayscale. The results indicate that the proposed model performs best with RGB images, achieving a Dice coefficient of 91.15%, which is notably higher compared to the segmentation of images in the other two color spaces. This research underscores the superior accuracy of using RGB images for the segmentation of arecanut bunches, offering valuable insights for applications in precision agriculture. Bunches ; Segmentation ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; Agricultural Engineering and Mechanization
Authors: Aminu Yusuf | Nevra Bayhan | Hasan Tiryaki | Sedat Balllikaya
Source: Konya Journal of Engineering Sciences (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING AS A POWERFUL TOOL FOR PERFORMANCE PREDICTION AND OPTIMIZATION OF CONCENTRATED PHOTOVOLTAIC-THERMOELECTRIC SYSTEM Because there is a critical necessity to ensure the optimal operation of concentrated photovoltaic-thermoelectric (CPV-TE) systems, various optimization methods such as Paretosearch (PS), Multi-objective genetic algorithm (MOGA), and the hybrid Goal Attainment – Multi-objective genetic algorithm (GOAL-MOGA) are commonly employed. These approaches aim to enhance both the output power and energy efficiency of CPV-TE systems. By combining the Pareto fronts generated by MOGA and GOAL-MOGA, 19 distinct machine learning (ML) algorithms were trained. The findings demonstrate that the Artificial Neural Network (ANN) ML algorithm outperforms others, displaying an average prediction error of 0.0692% on the test dataset. In addition to its prediction capability, the ANN-based ML model can be viewed as an optimization model since it produces optimized outputs similar to those from MOGA and GOAL-MOGA. The ANN-based ML algorithm performs better when trained on a combined dataset from both MOGA and GOAL-MOGA compared to using either MOGA or GOAL-MOGA alone. To enhance the optimization capability of the ANN-based ML algorithm further, more Pareto fronts from other optimization techniques can be added. Genetic algorithm ; Photovoltaic system ; Computer science ; Artificial neural network ; Multi-objective optimization ; Pareto principle ; Pareto optimal ; Energy (signal processing) ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Engineering ; Mathematics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Solar Radiation and Photovoltaics
Authors: K. V. Andryukov
Source: International Journal of Applied and Fundamental Research (Международный журнал прикладных и фундаментальных исследований) (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

QUANTUM CHEMICAL CALCULATIONS IN SEARCH OF THE “STRUCTURE – ANTI-INFLAMMATORY ACTIVITY” MODEL USING THE AK_QSAR PROGRAM IN A SERIES OF AMIDES AND HYDRAZIDES OF N– 2-FURANOYL SUBSTITUTED ANTHRANILIC ACIDS Quantitative structure–activity relationship ; Quantum chemical ; Anthranilic acid ; Series (stratigraphy) ; Chemistry ; Quantum ; Computational chemistry ; Organic chemistry ; Stereochemistry ; Molecule ; Physics ; Biology ; Quantum mechanics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemical synthesis and pharmacological studies
Authors: P. Cushman | M. Fritts | Andrew Chambers | Arijit Roy | Tao Li
Source: arXiv (Cornell University) (preprint, 2024-04-17)
Field: Materials Science — Materials Chemistry

Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS Background reduction in the SuperCDMS dark matter experiment depends on removing surface events within individual detectors by identifying the location of each incident particle interaction. Position reconstruction is achieved by combining pulse shape information over multiple phonon channels, a task well-suited to machine learning techniques. Data from an Am-241 scan of a SuperCDMS SNOLAB detector was used to study a selection of statistical approaches, including linear regression, artificial neural networks, and symbolic regression. Our results showed that simpler linear regression models were better able than artificial neural networks to generalize on such a noisy and minimal data set, but there are indications that certain architectures and training configurations can counter overfitting tendencies. This study will be repeated on a more complete SuperCDMS data set (in progress) to explore the interplay between data quality and the application of neural networks. Modular design ; Detector ; Computer science ; Artificial intelligence ; Solid-state ; Machine learning ; Pattern recognition (psychology) ; Engineering ; Operating system ; Engineering physics ; Telecommunications Machine Learning in Materials Science
Authors: Viseth Putsaccada | Jittraporn Wongsa‐Ngam | Naoto Ohtake | Monnapas Morakotjinda | Rungtip Krataithong | Nattaya Tosangthum | Thanyaporn Yotkaew | Ruangdaj Tongsri
Source: AIP conference proceedings (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing ductility of sintered Fe-Ni-Mo-Mn-Si-C alloy by Co addition This research aims to investigate the effect of cobalt (Co) on accelerating the bainitic transformation in sintered Fe-Ni-Mo-Mn-Si-C alloys.Experimental sintered specimens were prepared from the mixtures of pre-alloyed Fe-4.00Ni-0.50Mo-0.2Mnpowder with a fixed 4.00 wt% silicon carbide and varied Co contents (0.50 to 3.00 wt%) using a traditional press and sinter process.Sintering was conducted in a vacuum furnace at 1250 C for 45 minutes and under slow cooling in the sintering furnace.It was found that Co strongly influenced the kinetics of bainite transformation in sintered Fe-Ni-Mo-Si-C-(Co) alloys.Tensile strength and hardness of sintered alloys increased with Co content.High elongation values were obtained in sintered specimens with high Co contents.The increases of tensile strength and ductility with Co content were attributed to accelerated bainitic ferrite formation and retained austenite stability, respectively. Materials science ; Sintering ; Metallurgy ; Ultimate tensile strength ; Alloy ; Ductility (Earth science) ; Austenite ; Ferrite (magnet) ; Carbide ; Cobalt ; Elongation ; Bainite ; Silicon carbide ; Microstructure ; Composite material ; Creep Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials ; Advanced materials and composites
Authors: Bishwajit Kar | Plabon Paul | Md Arshadur Rahman | Mohammad Jane Alam Khan
Source: arXiv (Cornell University) (preprint, 2024-01-06)
Field: Materials Science — Materials Chemistry

A Molecular Dynamics Study of Mechanical Properties of Vertically Stacked Silicene/MoS2 van der Waals Heterostructure Silicene is an intriguing silicon allotrope with a honeycomb lattice structure similar to graphene with slightly buckled geometry. Molybdenum disulfide (MoS2), on the other hand, is a significant 2D transition metal dichalcogenide that has demonstrated promise in a variety of applications. Van der Waals heterostructures, which are created by stacking distinct 2D crystals on top of each other, are becoming increasingly important due to their unique optoelectronic and electromechanical properties. Using molecular dynamics simulations, the mechanical characteristics of vertically stacked Silicene/MoS2 van der Waals heterostructures are examined in this study. The response and structural stability of the heterostructures at various loading orientations and temperatures are given particular attention. The research findings highlight that the fracture strength of the Silicene/MoS2 heterostructure decreases by 40% in both armchair and zigzag orientations when the temperature is raised from 100K to 600K. Furthermore, a linear decrease in Young's modulus is observed as temperature rises. It is noteworthy that the Rule of Mixture (ROM) predictions for Young's Moduli are observed to be marginally lower than the simulation results. The analyses reveal that the silicene layer fractures first under both loading directions shows crack propagation at +-60°in the armchair and predominantly perpendicular in zigzag, followed by subsequent MoS2 layer failure. The study also shows that the MoS2 layer largely determines the elastic properties of the heterostructure, whereas the silicene layer primarily dictates the failure of the heterostructure. These findings offer an in-depth understanding of the mechanical properties of Silicene/MoS2 heterostructures, with significant implications for their use in cutting-edge nanoelectronics and nanomechanical systems. Silicene ; Heterojunction ; van der Waals force ; Materials science ; Zigzag ; Graphene ; Condensed matter physics ; Molecular dynamics ; Molybdenum disulfide ; Density functional theory ; Nanotechnology ; Composite material ; Computational chemistry ; Optoelectronics ; Chemistry ; Geometry ; Physics ; Molecule Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites
Authors: Mina M. Aljuboury | Roaa Mohammed Muneer
Source: ARCHIVES OF ENGINEERING KNOWLEDGE (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Sustainable Agriculture and Nanotechnology: Review Nanoparticles have emerged as a promising tool for sustainable agriculture and biosafety. This review article examines the historical background of agricultural nanoparticles and explores different nanoparticles types, including Nano-emulsions, quantum dots, carbon nano tubes, Nano-rods, and Nano-encapsulation technology. Additionally, the review examines recent advances in the agricultural nanoparticles application, such as Nano-pesticides and Nano-fertilizers, and their potential effects on biosafety. Furthermore, the article discusses future prospects and challenges of the nanoparticles using in sustainable agriculture and biosafety. The potential ecological effects of nanoparticles and the need for responsible regulation and oversight are also highlighted. Overall, this review article emphasizes the significant potential of nanoparticles in sustainable agriculture and biosafety and the need for continued research and responsible development to ensure safe and equitable distribution of its benefits. Biosafety ; Sustainable agriculture ; Agriculture ; Sustainable development ; Sustainability ; Sustainable Agriculture Innovation Network Nanoparticles: synthesis and applications ; Polymer-Based Agricultural Enhancements ; Carbon and Quantum Dots Applications
Authors: Siyu Cen | Bangti Jin | Xi‐Yao Li | Zhiming Zhou
Source: arXiv (Cornell University) (preprint, 2024-11-30)
Field: Materials Science — Materials Chemistry

Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks In this work we develop a novel algorithm, termed as mixed least-squares deep neural network (MLS-DNN), to recover an anisotropic conductivity tensor from the internal measurements of the solutions. It is based on applying the least-squares formulation to the mixed form of the elliptic problem, and approximating the internal flux and conductivity tensor simultaneously using deep neural networks. We provide error bounds on the approximations obtained via both population and empirical losses. The analysis relies on the canonical source condition, approximation theory of deep neural networks and statistical learning theory. We also present multiple numerical experiments to illustrate the performance of the method, and conduct a comparative study with the standard Galerkin finite element method and physics informed neural network. The results indicate that the method can accurately recover the anisotropic conductivity in both two- and three-dimensional cases, up to 10\% noise in the data. Anisotropy ; Conductivity ; Artificial neural network ; Least-squares function approximation ; Deep neural networks ; Materials science ; Artificial intelligence ; Mathematics ; Physics ; Computer science ; Optics ; Statistics Machine Learning in Materials Science